ARTICLE | doi:10.20944/preprints201804.0154.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: DC-DC power converter; fault tolerance; multilevel converter; switched-capacitor network
Online: 11 April 2018 (14:18:54 CEST)
A modular switched-capacitor (SC) DC-DC converter (MSCC) is introduced in this paper. It is designed to boost a low input voltage to a high voltage level and can be applied for photovoltaics and electric vehicles. This topology has high extensibility for high voltage gain output. The merits of the converters also lie in the fault tolerance operation and the voltage regulation with a minimum change in the duty ratio. Those features are built in when designing the modules and then integrating these into the DC-DC converter. Converter performance including voltage gain, voltage and current stress are focused and tested. The converter is modelled analytically, and its control algorithm is analyzed in detailed. Both simulation and experiment are carried out to verify the topology under normal operation and fault mode operation.
ARTICLE | doi:10.20944/preprints202308.0642.v1
Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: IoT networksServices Layer; Gateway Layer; Rectangular and Interstitial Mesh networks; Computing Fault tolerance
Online: 9 August 2023 (02:56:45 CEST)
Most IoT systems meant for implementing mission-critical systems are multi-layered based. The majority of the computing is done in the services and gateway layer. The Gateway layer connects the Internal section of the IoT to the cloud through the Internet. The failure of any node between the services servers and the gateways will isolate the entire network leading to zero tolerance. A networking topology is required to connect the service servers to the gateways, yielding 100% fault tolerance. Empirical formulation of the model chosen to connect the service’s servers to gateways through routers is required to compute the fault tolerance of the network. Rectangular and Interstitial Mesh have been proposed in this paper to connect the service servers to the gateways through the servers that yield 0.999 fault tolerance of the IoT network. An empirical formulation is also presented using which the Fault tolerance of the IoT network can be computed. An enhancement of 11% in the fault tolerance capability of an IoT network has been achieved by implementing Rectangular and Interstitial Mesh in the gateway layer of the network.
ARTICLE | doi:10.20944/preprints201803.0057.v1
Subject: Engineering, Marine Engineering Keywords: fault-tolerant control; thruster fault; fault detection and isolation; fault accommodation; ROV; remotely operated vehicle; underwater vehicle
Online: 8 March 2018 (02:45:28 CET)
This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault detection units (FDUs), associated with each thruster, to monitor their state. The robust, reliable and adaptive FDUs use a model-free pattern recognition neural network (PRNN) to detect internal and external faulty states of the thrusters in real time. The FA subsystem combines information provided by the FDI subsystem with predefined, user-configurable actions to accommodate partial and total faults and to perform an appropriate control reallocation. Software-level actions include penalisation of faulty thrusters in solution of control allocation problem and reallocation of control energy among the operable thrusters. Hardware-level actions include power isolation of faulty thrusters (total faults only) such that the entire ROV power system is not compromised. The proposed FTC system is implemented as a LabVIEW virtual instrument (VI) and evaluated in virtual (simulated) and real-world environments. The proposed FTC module can be used for open frame ROVs with up to 12 thrusters: eight horizontal thrusters configured in two horizontal layers of four thrusters each, and four vertical thrusters configured in one vertical layer. Results from both environments show that the ROV control system, enhanced with the FDI and FA subsystems, is capable of maintaining full 6 DOF control of ROV in the presence of up to 6 simultaneous total faults in the thrusters. With the FDI and FA subsystems in place the control energy distribution of the healthy thrusters is optimised so that the ROV can still operate in difficult conditions under fault scenarios.
ARTICLE | doi:10.20944/preprints202304.1034.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: ITSC fault; traction motor; fault diagnosis; apFFT; SVM
Online: 27 April 2023 (04:29:04 CEST)
Abstract: The EMU(electric multiple units) traction motors are powered by converters. The PWM(pulse width modulation) voltage increases the voltage stress borne by the motor insulation system, making the ITSC(inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power was proposed in the article to evaluate the non-metallic ITSC faults degree. The apFFT(all phase FFT) time-shift phase difference correction with double Hanning-windows is used to calculate the fundamental frequency of the traction motor's ZSVC（zero se-quence voltage component）, the fundamental amplitudes of ZSVC and three-phase current. The five parameters are used as fault features to train the SVM (support vector machine)fault diagnosis model. The SVM hyper-parameters C and g are optimized by K-CV (K fold cross-validation) and grid search methods. The experimental verification was carried out by the EMU electric traction simulation experimental platform. According to the non-metallic degree index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training data set and 93.33 % on the test data set. There was no misclassification between normal and serious ITSC fault samples.
ARTICLE | doi:10.20944/preprints202208.0160.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: fault injection; functional safety; automotive applications; fault tolerance
Online: 8 August 2022 (13:41:10 CEST)
A common requirement of embedded software in charge of safety tasks is to guarantee the identification of those Random Hardware Failures (RHFs) that can affect digital components. RHFs are unavoidable. For this reason, functional safety standards, like the ISO 26262 devoted to automotive applications, require embedded software designs able to detect and eventually mitigate them. For this purpose, various software-based error detection techniques have been proposed over the years, focusing mainly on detecting Control Flow Errors. Many Control Flow Checking (CFC) algorithms have been proposed to accomplish this task. However, applying these approaches can be difficult because their respective literature gives little guidance on the their practical implementation in high-level programming languages, and they have to be implemented in low-level code, e.g., assembly. Moreover, the current trend in the automotive industry is to adopt the so-called Model-Based Software Design approach, where an executable algorithm model is automatically translated into C or C++ source code. This paper presents two novelties: firstly, the compliance of the experimental data on the capabilities of Control Flow Checking (CFC) algorithms with the ISO 26262 automotive functional safety standard; Secondly, by the implementation of the CFC algorithm in the application behavioral model is automatically translated. There is no need to modify the code generator. The assessment was performed using a novel fault injection environment targeting a RISC-V (RV32I) microcontroller.
ARTICLE | doi:10.20944/preprints202309.0237.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: Turkey earthquake; peak ground acceleration; seismogenic fault; near-fault effect; fault locking segment effect; trampoline effect
Online: 5 September 2023 (05:20:36 CEST)
A Mw7.8 earthquake struck Turkey on February 6, 2023, causing severe casualties and economic losses. This paper investigates the characteristics of strong ground motion and seismogenic fault of the earthquake. We collected and processed the strong ground motion records of 379 stations using Matlab, SeismoSignal and Surfer software, and obtained the peak ground acceleration (PGA) contour map. We analyzed the near-fault effect, the fault locking segment effect and the trampoline effect of the earthquake based on the spatial distribution of PGA, the fault geometry and slip distribution. We found that the earthquake generated a very strong ground motion concentration effect in the near-fault area, with the maximum PGA exceeding 2000cm/s2. However, the presence of fault locking segments influenced the spatial distribution of ground motion, resulting in four significant PGA high-value concentration areas at a local dislocation, a turning point and the end of the East Anatolian Fault. We also revealed for the first time the typical manifestation of the trampoline effect in this earthquake, which was characterized by a large vertical acceleration with the positive direction significantly larger than the negative direction. This paper provides an important reference for understanding the seismogenic mechanism, damage mode, characteristics and strong earthquake law of Turkey earthquake.
ARTICLE | doi:10.20944/preprints202309.0950.v1
Subject: Engineering, Control And Systems Engineering Keywords: dynamic system; incipient fault; process monitoring; fault detection; MKPCA; DCCA
Online: 14 September 2023 (07:20:45 CEST)
incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.
ARTICLE | doi:10.20944/preprints202305.1623.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Goertzel algorithm; ITSC fault; traction motor; random forest; fault diagnosis
Online: 23 May 2023 (08:31:43 CEST)
The stator winding insulation system is the most critical and weak part of the EMU's (electric multiple unit) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent the fault from expanding into phase-to-phase or ground short-circuits. The TCU(traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is stationary. Three ITSC fault diagnostic conditions are based on different IGBTs control logic. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on random forest. The proposed method was validated using a simulation experimental platform for ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with the increase of ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location both in train and test samples are 100%.
ARTICLE | doi:10.20944/preprints201908.0029.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: multiple fault detection; rotating electrical machines; drive systems; multi-label classification; machine learning; fault severity; fault classifiers
Online: 2 August 2019 (10:44:06 CEST)
Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.
ARTICLE | doi:10.20944/preprints202201.0159.v1
Online: 12 January 2022 (09:50:31 CET)
A new method for short circuit fault location is proposed based on instantaneous signal measurement and its derivatives, and is based on the retardation phenomena. The difference between the times in which a signal is registered in two detectors is used to locate the fault. Although a description of faults in terms of a lumped circuit is useful for elucidating the methods for detecting the fault. This description will not suffice to describe the fault signal propagation hence a distributed models is needed which is given in terms of the telegraph equations. Those equations are used to derive a transmission line transfer function, and an exact analytical description of the short circuit signal propagating in the transmission line is obtained. The analytical solution was verified both by numerical simulations and experimentally.
ARTICLE | doi:10.20944/preprints202304.1140.v1
Subject: Engineering, Safety, Risk, Reliability And Quality Keywords: Aircraft fault diagnosis; knowledge graph; deep learning; fault knowledge extraction; question-answering system
Online: 28 April 2023 (08:00:31 CEST)
When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.
ARTICLE | doi:10.20944/preprints201909.0127.v1
Subject: Engineering, Energy And Fuel Technology Keywords: fault location; service restoration; particle swam optimization; microgrid; power flow; short-circuit fault
Online: 12 September 2019 (03:59:27 CEST)
This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.
ARTICLE | doi:10.20944/preprints202103.0650.v1
Subject: Chemistry And Materials Science, Biomaterials Keywords: palladium; palladium alloys; generalized stacking fault energy; twinnability; stacking fault energy; ab initio calculations
Online: 25 March 2021 (17:19:22 CET)
Generalized stacking fault energies of palladium alloys were calculated using the density functional theory. The stacking fault energy of palladium alloys is correlated with the valence electron of the transition metal element. The twinning tendency is also modified by the presence of an alloying element in the plane of deformation. The obtained results suggest that Pd –transition metal alloys with elements such as Cr, Mo, W, Mn, Re are expected to exhibit high work hardening rate due to the tendency to emit of the partial dislocations and mechanical twins, which results in increased strength and ductility.
ARTICLE | doi:10.20944/preprints202106.0365.v1
Subject: Engineering, Automotive Engineering Keywords: Unmanned aerial vehicle (UAV); faulty sensors; fault detection and isolation; abrupt fault; feedback linearization control
Online: 14 June 2021 (13:25:52 CEST)
A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate sensors' faults in real-time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter's dynamic model to detect faults in the navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real-time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter navigation sensors, and the helicopter tracks the desired trajectory without any interruption.
ARTICLE | doi:10.20944/preprints201812.0265.v1
Subject: Engineering, Control And Systems Engineering Keywords: fault diagnosis; analytical redundancy; fuzzy prototypes; neural networks; diagnostic residuals; fault reconstruction; wind turbine simulator
Online: 24 December 2018 (03:59:45 CET)
The fault diagnosis of wind turbine systems represent a challenging issue, especially for offshore installations, thus justifying the research topics developed in this work. Therefore, this paper addresses the problem of the fault diagnosis of wind turbines, and it present viable solutions of fault detection and isolation techniques. The design of the so--called fault indicator consists of its estimate, which involves data--driven methods, as they result effective tools for managing partial analytical knowledge of the system dynamics, together with noise and disturbance effects. In particular, the suggested data--driven strategies exploit fuzzy systems and neural networks that are employed to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, as they approximate the dynamic evolution of the system along time. The designed fault diagnosis schemes are verified via a high--fidelity simulator, which describes the normal and the faulty behaviour of an offshore wind turbine plant. Finally, by taking into account the presence of uncertainty and disturbance implemented in the wind turbine simulator, the robustness and the reliability features of the proposed methods are also assessed. This aspect is fundamental when the proposed fault diagnosis methods have to be applied to offshore installations.
ARTICLE | doi:10.20944/preprints201811.0632.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: fault line detection; data fusion; non-artificial setting; sound distance; fault distance; resonant grounding system
Online: 30 November 2018 (10:38:18 CET)
Fault line detection timely and accurately when single-phase-to-earth fault occurs in resonant grounding system is still a focus of research. This paper presents a new approach for fault detection based on data fusion and it has non-artificial setting. Firstly, the fault criterion for interphase difference energy ratio and time-frequency correlation coefficient of each line is proposed. Subsequently, the paper establish a coordinate system with the interphase difference energy ratio as X axis and the time-frequency correlation coefficient as Y axis, and it uses the Euclidean distance algorithm to get the characteristic distance of each line by fusing two-dimensional information. Finally, comparing the sound distance and the fault distance of each line to discriminate the fault line. Electromagnetic Transients Program (EMTP) simulation results and adaptability analysis have confirmed the effectiveness and reliability of the proposed scheme.
ARTICLE | doi:10.20944/preprints202011.0366.v1
Subject: Engineering, Automotive Engineering Keywords: Fault detection; Control Valve; Reliability, Prediction
Online: 13 November 2020 (09:23:39 CET)
Reliability assessment is an important component and tool used for process plants since the facility consists of many loops and instruments attached and operates based on each other availability, thus it requires a statistical method to visualize the reliability. The paper focuses on reliability assessment and prediction based on available statistical models such as normal, log-normal, exponential, and Weibull distribution. This paper also visualizes, which model fits best for assessment and prediction and also considers failure modes caused during a simulation mode process control operation. A simulation model is designed in this paper to observe the failure of the control valve causing stiction to visualize the failure modes and predict the best-fit model for reliability assessment.
ARTICLE | doi:10.20944/preprints201610.0063.v1
Subject: Engineering, Civil Engineering Keywords: dual steel frame; far field from fault; near field to fault; time history non-linear dynamic analysis
Online: 15 October 2016 (08:39:13 CEST)
This study sought to investigate steel frames’ performance with dual lateral loader system (Frame bending + bracings of divergent and convergent) in near and far filed to fault. In order to this, four categories of steel frame with dual system with 8, 10 and 12 story are designed with average formation based on existing seismic regulation in 2800 standard of Iran and tenth chapter of national regulations of construction (planning and performing steel construction). Time history non-linear dynamic analysis under the effect of near and far field earthquakes has been done on plan’s models. Then the maximum of floors’ dislocation, floors’ drift, roof dislocation, base shear and energy curves of frames are shown and compared with each other. All non-linear time history analyses have been accomplished using PERFORM 3D software.
ARTICLE | doi:10.20944/preprints202308.0905.v1
Subject: Engineering, Control And Systems Engineering Keywords: condition monitoring; soft sensors; fault diagnosis; modularization
Online: 11 August 2023 (08:05:07 CEST)
The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this are modular process plants that consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but available solutions are not applicable. The authors suggest an approach in which model-based symptoms are derived from few measurements and observers that are based on manufacturer knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcases the feasibility and the potential of this approach.
ARTICLE | doi:10.20944/preprints202304.0642.v1
Subject: Engineering, Safety, Risk, Reliability And Quality Keywords: gearbox; compound fault; attention mechanism; capsule network
Online: 20 April 2023 (09:58:25 CEST)
To realize the diagnosis of compound faults in gearboxes at different speeds, an "end-to-end" intelligent diagnosis method based on a deep neural network is proposed, named efficiency channel attention-capsule network (ECA-CN). First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, then embeds the efficient channel attention module to filter important fault features, then uses the capsule network to vectorize the feature space information, and finally calculates the correlation between different levels of capsules by the dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method was verified by the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63% and a standard deviation of 0.22%. In the comparison experiments with the traditional fault diagnosis method, the average accuracy of the ECA-CN method was improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that ECA-CN has a more competitive diagnostic performance.
ARTICLE | doi:10.20944/preprints202111.0325.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Community disaster resilience; Lembang Fault; Indonesia; Japan
Online: 18 November 2021 (13:43:52 CET)
Having experienced large-scale disasters between 2004 and 2006, the fatalities due to large-scale disasters in 2018 were still high. In contrast, disaster risk management (DRM) and CDR in Japan have been continuously improved. Thus, there is a need to develop CDR for supporting DRM in Indonesia by learning from the Japanese experience, particularly in a disaster-prone area without large-scale disaster experience. This research was a pilot project on the development of CDR in Indonesia. The case study was a geological hazard-prone Lembang Fault area. People’s perception was collected using structured interviews, while demographic and local economic data was acquired from official statistical publications. Satellite imageries were utilized to acquire natural and built environment and land use/land cover and their changes between 2019 and 2021. Although the degrees of social capital, risk knowledge including indigenous knowledge and past disaster experience were high, government interventions on DRM and land administration are required to develop CDR in Lembang Fault area. Organized community development is expected rather than to solely involve NGOs. Moreover, strategies to develop economic resilience are needed to allow the community to bounce back from future disaster. Finally, a detail baseline data should be collected to develop DRM strategy and CDR.
ARTICLE | doi:10.20944/preprints201904.0028.v1
Subject: Engineering, Civil Engineering Keywords: rockfall; susceptibility; GIS; rainfall; earthquake; fault; inventory
Online: 2 April 2019 (07:54:57 CEST)
The assessment of rockfall risks on human activities and infrastructure is of great importance. Rock falls pose a significant risk to a) transportation infrastructure b) inhabited areas and c) Cultural Heritage sites. The paper presents a method to assess rockfall susceptibility at national scale in Greece, using a simple rating approach and GIS techniques. An extensive inventory of rockfalls for the entire country was compiled for the period between 1935 and 2019. The rockfall events that were recorded are those, which have mainly occurred as distinct rockfall episodes in natural slopes and have impacted human activities, such as roads, inhabited areas and archaeological sites. Through a detailed analysis of the recorded data, it was possible to define the factors which determine the occurrence of rockfalls. Based on this analysis, the susceptibility zoning against rockfalls at national scale was prepared, using a simple rating approach and GIS techniques. The rockfall susceptibility zoning takes into account the following parameters: (a) the slope gradient, (b) the lithology, (c) the annual rainfall intensity, (d) the earthquake intensity and (e) the active fault presence. Emphasis was given on the study of the earthquake effect as a triggering mechanism of rockfalls. Finally, the temporal and spatial frequency of the recorded events and the impact of rockfalls on infrastructure assets and human activities in Greece were evaluated.
ARTICLE | doi:10.20944/preprints201902.0253.v1
Subject: Social Sciences, Sociology Keywords: Disasters, Preparedness, Lembang Fault, Community Base, School
Online: 27 February 2019 (11:55:21 CET)
This research was conducted on the Maribaya Timur school community in Lembang Subdistrict, West Bandung Regency, Indonesia, which is an active community in the area that is threatened by the potential for earthquake disasters due to the active Lembang fault. Disaster risk reduction efforts are pursued through increasing school-based preparedness that involves members of the school community, surrounding communities and various institutions that are associated with reducing the risk of school-based earthquake. Increasing preparedness against earthquakes focuses more on aspects of capacity building of school communities in reducing disaster risk, while aspects of vulnerability and threats have not been the focus of disaster risk reduction. The steps taken refer to the element of preparedness by aligning with the conditions, needs and potential that exist in the school community. Theoretically, if the school community has preparedness to face an earthquake disaster, the risk of earthquake disaster in the school community will be reduced so that it can minimize losses, victims and suffering that will be caused by the earthquake disaster.
ARTICLE | doi:10.20944/preprints201805.0170.v1
Subject: Engineering, Mechanical Engineering Keywords: machine fault diagnosis; rotordynamics; rub; aeroderivative turbines; accelerometers; early fault detection; fourier analysis; real cepstrum; continuous wavelet transform
Online: 10 May 2018 (16:10:11 CEST)
A common fault in turbomachinery is rotor--casing rub. Shaft vibration, measured with proximity probes, is the most powerful indicator of rotor-stator rub. However, in machines such as aeroderivative turbines, with increasing industrial relevance in power generation, constructive reasons prevent the use of those sensors, being only acceleration signals at selected casing locations available. This implies several shortcomings in the characterization of the machinery condition, associated with a lower information content about the machine dynamics. In this work we evaluate the performance of the Continuous Wavelet Transform to isolate the accelerometer signal features that characterize rotor-casing rub in an aeroderivative turbine. The evaluation is carried out on a novel rotor model of a rotor flexible casing system. Due to damped transients and other short-lived features that rub induces in the signals, the Continuous Wavelet Transform proves being more effective than both Fourier and Cepstrum Analysis. This creates the chance for enabling early fault diagnosis of rub before it may cause machine shutdown or damage.
ARTICLE | doi:10.20944/preprints202207.0247.v2
Subject: Engineering, Control And Systems Engineering Keywords: fault detection; retraction/extension (R/E) hydraulic system; bond graph-linear fractional trans-formation technique; interval analytic redundancy relations; uncertainty; fault signature matrix; residuals; thresholds
Online: 17 August 2022 (03:53:54 CEST)
Various factors, such as uncertainty of component parameters and uncertainty of sensor meas-urement values, contribute to the difficulty of fault detection in the landing gear retrac-tion/extension hydraulic system. In this paper, we introduce linear fractional transformation technology and uncertainty analysis theory for the construction of the diagnostic bond graph of the landing gear retraction/extension hydraulic system. In this way, interval analytical redundancy relations and fault signature matrix can be derived. Using the fault signature matrix, existing faults of the system can be preliminarily detected and isolated. Additionally, interval analytical re-dundancy relations can be used to detect system faults in detail, and cases analysis can be carried out to determine if the actuator is externally or internally leaky, and if the landing gear selector valve is reversing stuck. Compared to the traditional analytical redundancy relations, this method takes into account the negative factors of uncertainty; and compared to the traditional absolute diagnostic threshold, the interval diagnostic threshold is more accurate and sensitive.
ARTICLE | doi:10.20944/preprints202308.0919.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: stochastic fault surface; earthquake source model; elasticity theory
Online: 11 August 2023 (09:02:09 CEST)
It is known that the source of a tectonic earthquake in the framework of the theory of elasticity and viscoelasticity is considered as a displacement along a certain fault surface. Usually, when describing the source, the geometry of the fault surface is simplified to a flat rectangular area. The displacement vector is assumed to be constant. In this paper, we propose a model of an earthquake source in the form of a displacement with a constant vector along a stochastic surface. A number of standard assumptions were made when modeling. We take into account only the elastic properties of the medium. We consider the Earth’s crust as a half-space and assume that the medium is homogeneous and isotropic. For the mathematical description of the earthquake source, we use the classical force equivalent of displacement along the fault. This is the distribution of double pairs of forces. The field of displacements under the action of body forces is found through a combination of Mindlin nuclei of strain. The paper presents solutions for strike-slip fault. To obtain a stochastic fault surface, we propose to introduce a random deformation of a rectangular flat surface. The paper presents the results of a computational experiment comparing the levels and regions of relative deformations of the Earth’s crust in the case of displacement along a flat rupture surface and along a stochastic one. In the case of a stochastic surface, the regions of relative deformations become asymmetric. The developed model will help to indirectly take into account various mechanical properties of the Earth’s crust when modeling deformations caused by earthquakes.
ARTICLE | doi:10.20944/preprints202305.1227.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: earthquake; active fault; meltwater flow; hydrological anomaly; correlation
Online: 17 May 2023 (10:25:45 CEST)
Future climate change is expected to affect components of the hydrological cycle. However geologic factors can sometimes be more important than the climate with regarding to change hydrological processes. The Keriya River is a large glacier-fed river in the western Tibet Plateau, its upper reaches flow across active fault zones, which have experienced four medium-sized earthquakes within four months 1974. These active faults have made difficult to identify normal meltwater changes using a snow-ice melt model. Because of the seismic events, the river has experienced substantial changes in their drainage systems and complex hydrological processes. Both the Mann-Kendall trend test and a correlation analysis were applied to detect the monthly patterns of stream flows, the change points and the correlations among both summer discharge and air temperature before and after seismic events. We found significant correlations between monthly air temperature and meltwater flow in the summer June during the pre-earthquake of 1958-1974, but none in July and August in the post-earthquake of 1975-1980, and we found no correlation following the earthquake. The correlations were the inverse by losing the former and setting up the latter alternately since the multiple earthquakes. Clearly, the earthquakes and changes in the faults can greatly induce hydrological processes.
ARTICLE | doi:10.20944/preprints202102.0288.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Adaptive Protection; Fault Detection; Microgrids; Directional Overcurrent Relay
Online: 11 February 2021 (16:19:02 CET)
In this research work, an adaptive scheme for the coordinated protection of AC Microgrids using directional overcurrent (DOCR) relays is presented. Protection of AC MGs is a complex and challenging issue due to the dynamic nature of the network including, a) its capability to reconfigure the modes of operation ranging from grid-connected to the islanded mode, c) bidirectional-power flow capability, and c) integration of intermittent renewable energy resources with real-time variations in the resource availability. Consequently, the fault current contributions may largely vary depending upon the incident conditions on the network. Conventional protection schemes, generally designed for radial networks, and unidirectional power flow from the source end to the load may either mal-operate or exhibit very poor performance, if not adapted according to the dynamic conditions of the network. To address this issue, a communication-based adaptive protection scheme capable to adapt its settings according to the generation resource availability and network configuration is presented in this work. The proposed scheme consists of an intelligent central protection unit (ICPU) capable to update the settings and communicate it to the individual relays based on the pre-calculated offline settings. The directional overcurrent relays employed in the scheme use two-stage settings, i.e. definite time and inverse definite minimum time characteristics for the effective coordination among the downstream and upstream relays. An adaptive algorithm for ICPU operation is presented and a case study is implemented for a modified IEEE 9-bus system using DigSilent Power factory. The results for various scenarios including, a) grid-connected mode of operation, b) islanded mode of operation, and c) variable distributed generation mode are obtained and compared to the static scheme, which validates the effectiveness of the proposed scheme.
ARTICLE | doi:10.20944/preprints202005.0347.v1
Subject: Engineering, Mechanical Engineering Keywords: deep learning; maximum mean discrepancy; gearbox; fault detection
Online: 22 May 2020 (05:21:56 CEST)
In the past years, various intelligent machine learning and deep learning algorithms have been developed and widely applied for gearbox fault detection and diagnosis. However, the real-time application of these intelligent algorithms has been limited, mainly due to the fact that the model developed using data from one machine or one operating condition has serious diagnosis performance degradation when applied to another machine or the same machine with a different operating condition. The reason for poor model generalization is the distribution discrepancy between the training and testing data. This paper proposes to address this issue using a deep learning based cross domain adaptation approach for gearbox fault diagnosis. Labelled data from training dataset and unlabeled data from testing dataset is used to achieve the cross-domain adaptation task. A deep convolutional neural network (CNN) is used as the main architecture. Maximum mean discrepancy is used as a measure to minimize the distribution distance between the labelled training data and unlabeled testing data. The study proposes to reduce the discrepancy between the two domains in multiple layers of the designed CNN to adapt the learned representations from the training data to be applied in the testing data. The proposed approach is evaluated using experimental data from a gearbox under significant speed variation and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other domain adaptation methods demonstrates the superiority of the proposed method.
ARTICLE | doi:10.20944/preprints201811.0299.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: complex systems; self-diagnosis; intermittent fault; diagnosis procedure
Online: 13 November 2018 (05:27:55 CET)
System level diagnosis is an abstraction of high level and, thus, its practical implementation to particular cases of complex systems is the task which requires additional investigations, both theoretical and modeling. Mostly, diagnosis at system level intends to identify only permanently faulty units. In the paper, we consider the case when both permanently and intermittently faulty units can occur in the system. Identification of intermittently faulty units has some specifics which we have considered in this paper. We also suggest the method which allows distinguishing among different types of intermittent faults. Diagnosis procedure was suggested for each type of intermittent faults.
REVIEW | doi:10.20944/preprints201810.0059.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: code smells; code fault-proneness; bugs; software evolution
Online: 3 October 2018 (14:56:48 CEST)
Context: Code smells are associated with poor design and programming style that often degrades code quality and hampers code comprehensibility and maintainability. Goal: Identify reports from the literature that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the~stated goal. Results: The SLR includes selected studies from July 2007 to September 2017 which analyzed the source code for open source and proprietary projects, as well, as several code smells and anti-patterns. The results of this SLR show that 24 code smells are more influential in the occurrence of bugs according to 16 studies. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques and procedures applied to analyze the influence. Conclusion: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.
ARTICLE | doi:10.20944/preprints201801.0102.v1
Subject: Engineering, Mechanical Engineering Keywords: Planetary gear; Fault diagnosis; CEEMDAN; Permutation entropy; ANFIS
Online: 11 January 2018 (11:54:12 CET)
For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90.75%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.
ARTICLE | doi:10.3390/sci2040061
Subject: Engineering, Industrial And Manufacturing Engineering Keywords: industry4.0; fault detection; fault diagnosis; random forest; diagnostic graph; distributed diagnosis; model-based; data-driven; hybrid approach; hydraulic test rig
Online: 24 September 2020 (00:00:00 CEST)
In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.
ARTICLE | doi:10.20944/preprints202007.0548.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: industry4.0; fault detection; fault diagnosis; random forest; diagnostic graph; distributed diagnosis; model-based; data-driven; hybrid approach; hydraulic test rig
Online: 23 July 2020 (11:26:41 CEST)
In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.
ARTICLE | doi:10.20944/preprints202308.0739.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; fault diagnosis; adaptive activation function; pumping unit
Online: 9 August 2023 (07:22:09 CEST)
Due to the complex underground environment, pumping machines are prone to produce numerous failures. The indicator diagrams of faults are similar in a certain degree, which produces indistinguishable samples. As the samples increases, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. For accurately and quickly judging the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the ReLU activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared with the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity, can improve the generalization performance of deep learning model, the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well embedded in other neural networks.
ARTICLE | doi:10.20944/preprints202304.0128.v1
Subject: Engineering, Mechanical Engineering Keywords: vibrations; condition monitoring; rolling element bearings; signals; fault diagnosis
Online: 7 April 2023 (13:13:47 CEST)
Condition monitoring is today a crucial and unavoidable practice related to the use of widespread mechanical components as rolling element bearings. Despite the diffusion of new data-driven techniques to analyze vibrational signals and detect the state of health of the system, mathematical tools, deriving more or less directly from the Fourier transform and defined either on the time or the frequency domain or on a combination of them (i.e. signal-based techniques), remain still essential and irreplaceable in the light of the deep and detailed information they provide. In this paper the diagnostic efficacy of an ample spectrum of these methods is investigated, applying them to study faults occurrence in the first bearing dataset released by NASA IMS Center. Thanks to their analytical definition, which is formulated taking into consideration to some extent the mathematical nature of the signal, faults signatures can be clearly identified and their temporal development can be fully traced, confirming their ability as powerful means to constantly monitor the state of a system even in real-time applications.
ARTICLE | doi:10.20944/preprints202112.0460.v1
Subject: Engineering, Industrial And Manufacturing Engineering Keywords: Fault Detection; 3D Printer; Error Detection; FFF; Contact Sensor
Online: 29 December 2021 (09:10:49 CET)
Desktop fused filament fabrication (FFF) 3D printers have been growing in popularity among hobbyist and professional users as a prototyping and low-volume manufacturing tool. One issue these printers face is the inability to determine when a defect has occurred rendering the print unusable. Several techniques have been proposed to detect such defects but many of these approaches are tailored to one specific fault (e.g., filament runout/jam), use expensive hardware such as laser distance sensors, and/or use machine vision algorithms which are sensitive to ambient conditions, and hence can be unreliable. This paper proposes a versatile, reliable, and low-cost system, named MTouch, to detect millimeter-scale defects that tend to make prints unusable. At the core of MTouch is an actuated contact probe designed using a low-power solenoid, magnet, and hall effect sensor. This sensor is used to check for the presence, or absence, of the printed object at specific locations. The MTouch probe demonstrated 100% reliability, which was significantly higher than the 74% reliability achieved using a commercially available contact probe (the BLTouch). Additionally, an algorithm was developed to automatically detect common print failures such as layer shifting, bed separation, and filament runout using the MTouch probe. The algorithm was implemented on a Raspberry Pi mini-computer via an Octoprint plug-in. In head-to-head testing against a commercially available print defect detection system (The Spaghetti Detective), the MTouch was able to detect faults 44% faster on average while only increasing the print time by 8.49%. In addition, MTouch was able to detect faults The Spaghetti Detective was unable to identify such as layer shifting and filament runout/jam.
ARTICLE | doi:10.20944/preprints202111.0548.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Failure Prediction; Fault-tolerance; Cloud Computing; Artificial Intelligence; Reliability
Online: 29 November 2021 (15:39:23 CET)
Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.
ARTICLE | doi:10.20944/preprints202008.0590.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: deformation; earthquake; InSAR; inversion; fault; convergence; Apulia; Epirus; Greece
Online: 27 August 2020 (04:06:29 CEST)
We identify the source of the Mw = 5.6 earthquake that hit west-central Epirus on March 21, 2020 00:49:52 UTC. We use synthetic aperture radar interferograms tied to one permanent Global Navigation Satellite System (GNSS) station (GARD). We model the source by inverting the INSAR displacement data. The inversion model suggests a shallow source on a low-angle fault (39°) dipping towards east with a centroid depth of 8.5 km. The seismic moment deduced from our model agrees with those of the published seismic moment tensors. This geometry is compatible with the Margariti thrust fault within the collision zone between Apulia and Eurasia. We also processed new GNSS data and estimate a total convergence rate between Apulia and Eurasia of 8.9 mm yr-1 , of which shortening of the crust between the Epirus coastal GNSS stations and station PAXO in the Ionian Sea is equivalent to ~ 50% of it or 4.6 mm yr−1. A 60-km wide deformation zone takes up nearly most of the convergence between Apulia-Eurasia, trending N318°E. Its central axis runs along the southwest coast of Corfu, along the northeast coast of Paxos, heading toward the northern extremity of the Lefkada island.
ARTICLE | doi:10.20944/preprints201903.0250.v1
Subject: Engineering, Mechanical Engineering Keywords: fault diagnosis, stochastic resonance, periodic potential, underdamped, weak signal
Online: 27 March 2019 (08:49:38 CET)
The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.
REVIEW | doi:10.20944/preprints202302.0209.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence system; resilience; robustness; fault tolerance; graceful degradation; do-main-adaptation; meat-learning; adversarial attack; fault injection; concept drift; resilience as-sessment
Online: 13 February 2023 (09:07:36 CET)
Artificial intelligence systems are increasingly becoming a component of security-critical applications. The protection of such systems from various types of destructive influences is thus a relevant area of research. The vast majority of previously published works are aimed at reducing vulnerability to certain types of disturbances or implementing certain resilience properties. At the same time, the authors either do not consider the concept of resilience as such, or their understanding varies greatly. This work presents a formalized definition of resilience and its characteristics for artificial intelligence systems from a systemic point of view. It systematizes ideas and approaches to building resilience to various types of disturbances. Taxonomy of resilience of artificial intelligence systems to destructive disturbances is proposed. Approaches and technologies for complex protection of intelligent systems, issues of their resource efficiency and other open research issues are considered. Approaches of resilience assessment for artificial intelligence system are also analyzed and recommendations are provided for their implementation.
ARTICLE | doi:10.20944/preprints202309.2027.v1
Subject: Electrical And Electronic Engineering, Engineering Keywords: triple-network convergence; fault recovery; TD3 algorithm; DoS attack; resilience
Online: 29 September 2023 (08:35:10 CEST)
Recently, the triple-network convergence system (TNCS) has emerged from the deep integration of the power grid, transportation network, and information network. Fault recovery research in the TNCS is important since this system's complexity and interactivity can expand the faults scale and increase faults impact. Currently, fault recovery focuses primarily on single power grids and cyber-physical systems, but there are certain shortcomings, such as ignoring uncertainties including generator start-up failures and the occurrence of new faults during recovery, energy supply-demand imbalances leading to system security issues and communication delay caused by network attacks. In this study, we propose a recovery method based on the improved TD3 algorithm, factoring in shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay and an improved Actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefit in power grid failure scenarios and demonstrates a strong resilience against communication delay caused by DoS attacks.
ARTICLE | doi:10.20944/preprints202308.1946.v1
Subject: Environmental And Earth Sciences, Sustainable Science And Technology Keywords: Sustainable planning; sustainable monitoring; remote sensing; GIS; lineaments; fault; fracture
Online: 29 August 2023 (10:05:14 CEST)
The construction of sustainable road and highway networks in the world, despite numerous feasibility, pre-feasibility and execution studies, are always confronted with the demands and vagaries of foreseeable and unforeseeable natural disasters. Studying cyclones, earthquakes, fracturing and landslide zones along roads is therefore a challenge for the sustainability of these infrastructures. In many countries around the world, the methods generally used for these studies are not only expensive and time-consuming, but the results obtained are not always efficient. This work examines whether Landsat 8 (with a high cloud level) and SRTM data can be used in both equatorial and coastal Central Africa zones to produce relevant mapping, locating fracture and landslide zones, in order to contribute not only to better road layout at lower cost and in a relatively short time, but also to better prevention of geological disasters that may occur on its network. To this end, a map of the main road network was produced and validated with field data, as well as the maps of the main unstable slopes, faults and fractures zones intersecting the road or highway network. These approaches are useful for sustainable planning, management, monitoring and extension of roads worldwide especially, in Central Africa.
REVIEW | doi:10.20944/preprints202305.0036.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: transformer fault diagnosis; information system theory; information entropy; machine learning
Online: 2 May 2023 (01:27:29 CEST)
Safe, high-quality and economical electric energy transportation is the basic requirements of modern power system operation. Transformer, as one of the core equipment of power system and national grid system, has the characteristics of diverse types, variable models and wide deployment. It is the basic equipment for power system to realize voltage change and electric energy distribution. Because the power system transformer needs to operate with load for a long time, the probability of failure is usually higher than that of other power equipment in general. This paper starts from the transformer fault and fault diagnosis. Firstly, the transformer fault types, traditional transformer fault diagnosis techniques and the advantages of machine learning in transformer fault diagnosis are reviewed. Secondly, the application of information system theory and information entropy in transformer fault diagnosis is introduced. Then it introduces machine learning technology, feature selection and extraction technology in transformer fault diagnosis, and machine learning algorithms such as support vector machine and extreme learning machine for transformer fault diagnosis. Finally, the potential development trend of information system theory, information entropy and machine learning in transformer fault diagnosis is forecasted. The combination of transformer fault prediction and machine learning algorithm is helpful for power system maintenance personnel to accurately predict the running state of power equipment, and also provides a new method and technology for the safe and reliable operation and regular maintenance of power system.
ARTICLE | doi:10.20944/preprints202210.0255.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: controller; centralized; distributed; CHIL; fault-tolerant; MMC; prototype; Opal-RT
Online: 18 October 2022 (07:30:28 CEST)
Centralized control algorithm limits the hardware flexibility of a Modular Multilevel Converter (MMC). Therefore, industrial MMC applications have started adopting distributed control structures. Even though distributed controller reduces a single point of failure risk compared to the centralized controller, the failure risk of the entire control systems increases due to the number of local controllers. However, the distributed controller can be programmed in such a way as to bypass the faulty local controller and take care of the power modules with the adjacent local controllers. This paper implements a modular distributed fault-tolerant controller for a scaled laboratory MMC prototype. Experimental results show that an MMC can operate without interruption, even under two local controller failures. Besides, the experimental results are verified with the Opal-RT, a real-time simulator with the same controller in a Control Hardware in Loop (CHIL) environment.
ARTICLE | doi:10.20944/preprints202205.0123.v1
Subject: Engineering, Energy And Fuel Technology Keywords: wind energy; digitalisation; collaboration; co-innovation; machine learning; fault detection
Online: 10 May 2022 (03:05:25 CEST)
In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method called the WeDoWind Ecosystem is developed and demonstrated. The method is centred around specific "challenges", which are defined by "challenge providers" within a topical "space" and made available to participants via a digital platform. The data required in order to solve a particular "challenge" is provided by the "challenge providers" under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the methods perform significantly better than EDP’s existing method in terms of Total Prediction Costs (saving up to €120,000). The WeDoWind Ecosystem is found to be a promising solution for enabling co-innovation in wind energy, providing a number of tangible benefits for both challenge and solution providers.
ARTICLE | doi:10.20944/preprints202005.0349.v1
Subject: Engineering, Mechanical Engineering Keywords: fault diagnosis; deep learning; domain adapta-tion; gearbox; current signal
Online: 22 May 2020 (05:37:13 CEST)
In the recent years, intelligent data-driven faultdiagnosis methods on gearboxes have been successfully developedand popularly applied in the industries. Currently, most ofthe machine learning techniques require that the training andtesting data are from the same distribution. However, thisassumption is difficult to be met in the real industries, sincethe gearbox operating conditions usually change in practice,which results in significant data distribution gap and diagnosticperformance deteriorations in applying the learned knowledgeon the new conditions. This paper proposes a deep learning-based domain adaptation method to address this issue. Theraw current signals are directly used as the model inputs fordiagnostics, which are easy to collect in the real industries andfacilitate practical applications. The maximum mean discrepancymetric is introduced to the deep neural network, the optimizationof which guarantees the extraction of generalized machineryhealth condition features across different operating conditions.The experiments on a real-world gearbox condition monitoringdataset validate the effectiveness of the proposed method, whichoffers a promising tool for cross-domain diagnosis in the realindustries.
ARTICLE | doi:10.20944/preprints201911.0261.v1
Subject: Engineering, Control And Systems Engineering Keywords: feature selection; locally linear embedding; regularization technology; bearing fault diagnosis
Online: 22 November 2019 (10:05:03 CET)
The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.
REVIEW | doi:10.20944/preprints201805.0474.v1
Subject: Engineering, Architecture, Building And Construction Keywords: building energy monitoring system; heat lost coefficient (hlc); fault sensor
Online: 31 May 2018 (11:38:30 CEST)
The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.
ARTICLE | doi:10.20944/preprints201805.0282.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: hydraulic fracturing; hollow cylinder; single fracture; fault activation; induced seismicity
Online: 22 May 2018 (05:04:47 CEST)
Pre-existing fracture and secondary cracks in rock mass are formed by natural power, such as magma condensed to igneous rocks and tectonic movement. The orientation and inclination of these fractures obey certain laws relating to the stress, temperature, minerals, water and so on. Therefore, cracks react differently under the same external loading on the condition of various inclination, fissure apertures, stiffness and joint roughness. To simulate the crack propagation, experiments on hollow cylinder cut by one oblique interface mimicking single fracture accumulated numerous data discovering the failure criterion in accordance with the Mohr-Coulomb criterion. And theory on the Terzaghi’s effective principle take an essential role in controlling the behavior of triggering fault. This paper introduced a series of oblique plane cutting the cylinder regarded as fractures at different inclination to concentrate on how the fracture characteristics effect the stress and strain distribution inside the specimen, especially, the relationship between displacement and water head. The key point of this numerical simulation is coupling the solid phase and the fluid phase, specifically, the mechanic and seepage field. According to the statics, curves referring to deformation and water head could be described as increasing lines. Besides, simulation on coupling solid phase and fluid phase can supply crucial evaluation on activating existing fault, and thus predicting induced seismicity in reservoirs or estimating damage in shale gas exploration.
ARTICLE | doi:10.20944/preprints201804.0188.v1
Subject: Engineering, Mechanical Engineering Keywords: variational mode decomposition; detrended fluctuation analysis; heavy gearbox; fault diagnosis
Online: 16 April 2018 (05:35:21 CEST)
The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.
ARTICLE | doi:10.20944/preprints201701.0132.v1
Subject: Engineering, Mechanical Engineering Keywords: intelligent fault diagnosis; convolutional neural networks; domain adaptation; anti-noise
Online: 30 January 2017 (12:15:03 CET)
Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.
ARTICLE | doi:10.20944/preprints202010.0496.v1
Subject: Engineering, Automotive Engineering Keywords: power system protection; renewable energy sources; fault elimination; power system security
Online: 23 October 2020 (15:37:19 CEST)
In the development of power systems it is indicated very often, that transformation of power systems should be carried out in accordance with the idea of energy democracy. This will develop energy communities, that are trying to meet energy needs by using local renewable generation sources. This may result with a temporary low load on the MV lines connecting the community grid and the power system. Such state may cause incorrect operation of power protection systems. This can cause an extended protection operation time, due to decision algorithms inactivity at low values of measurement currents. Therefore, the detailed MV lines overcurrent digital protection model and a dynamic model of the power network were developed. The simulation results are showing that the settings of the parameters activating the protection decision algorithms affect their operation time in dynamic conditions. The conclusion is that the development of the power protection automatics must be carried out in the same time (preferably in advance) with the change of the power system operation model. This is very important for future power systems with high penetration energy communities and renewable generation sources.
ARTICLE | doi:10.20944/preprints202008.0164.v1
Subject: Engineering, Mechanical Engineering Keywords: intelligent fault diagnosis; bearing; anti-noise; one-dimensional convolution neural network
Online: 6 August 2020 (11:42:44 CEST)
In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.
Subject: Engineering, Mechanical Engineering Keywords: diesel engine; fault diagnosis; variational mode decomposition; random forest; feature extraction
Online: 25 December 2019 (11:13:13 CET)
Diesel engines, as power equipment, are widely used in the fields of automobile industry, ship and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in time domain and angular domain, on which the current diagnosis methods based, are easily affected by working conditions or hard to extract accurate enough, as the diesel engine keeps running in transient and non-stationary process. This work arms at diagnosing this fault mainly based on frequency band features which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively，a decomposition technique based on improved variational mode decomposition is investigated in this work. As the connection between the features and the fault is fuzzy, the random forest algorithm is used to analyze the correspondence between features and faults. In addition, the feature dimension is reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition show that the method based on variational mode decomposition and random forest is capable to detect valve clearance fault effectively.
ARTICLE | doi:10.20944/preprints202308.1644.v1
Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Industry 4.0; Programming Model; Automated Code Generation; Ethernet Powerlink; CANopen; Fault-Tolerance
Online: 23 August 2023 (09:19:40 CEST)
As the demands in reliability and high-degree of automation in industrial applications are increasing exponentially, the current Industry 4.0 trend lies in migrating towards smarter technologies that increase flexibility and provide autonomous operation. To tackle this, manufacturers shifted towards Real-Time Ethernet communication which supports autonomous operation, faster sensor/actuator configuration and high degree of determinism. Nevertheless, the uninterrupted operating principle of legacy industrial systems as well as the system complexity and heterogeneity constitutes the transition to Real-Time Ethernet very challenging. This article proposes a novel method allowing to automate the generation of executable code for Real-Time Ethernet architectures based on high-level programming models. The method is based on a programming model and modular code artifacts for the hardware architecture, employing Real-Time Ethernet communication. Through an tool-supported algorithm the high-level model is linked to the architecture's communication primitives and interfaces. The tool-support reduces significantly the development and debugging effort for Real-Time Ethernet applications and increases the application reliability, since the generated code is based on verified programming models. Additionally, the tool creates dedicated files, which allow automated industrial network configuration without any human intervention. Finally, by embedding firewall policies into the code generation process, the method guarantees cyber-resilience for the Real-Time Ethernet architecture. We demonstrate the proposed method in Programmable Logic Controllers (PLCs) of a Public Power Corporation's Hydroelectric Power Plant, which control the temperature and rotor speed of a power generator. The results demonstrate the method's ability to generate rapidly trustworthy and fault-tolerant code for the autonomous plant operation. The hurdles in time and effort for developing, configuring and debugging Real-Time Ethernet applications can be minimized through a high-level programming model that allows automated generation of executable code that is reusable for similar applications. Such time and effort reduction may pave the way towards the Industry 4.0 area.
ARTICLE | doi:10.20944/preprints202307.2086.v1
Subject: Engineering, Telecommunications Keywords: ultra-low loss fibers; OTDR; wavelet transform; fault detection; high altitude areas
Online: 31 July 2023 (09:59:35 CEST)
Breakage and damage of fiber optic cable fibers seriously affect the normal operation of fiber optic networks and it is important to quickly and accurately determine the type and location of faults when they occur. The advantages of wavelet transform in singular signal detection and signal filtering are used to analyze Optical Time Domain Reflectometer (OTDR) curve signal and the fault detection method of fiber communication link with no relay and large span in high altitude area is given, which realizes the accurate detection and location of optical fiber communication link fault events under strong noise. The theoretical analysis is basically consistent with the experimental data. The accuracy of optical fiber fault location has been greatly improved in performance. The method has been directly applied to the field detection of ultra-long optical fiber links in high altitude areas, which has certain significance for the future real-time monitoring of optical fiber cables.
ARTICLE | doi:10.20944/preprints202306.0572.v1
Subject: Engineering, Marine Engineering Keywords: Marine Diesel Generator; reliability importance measures; fault identification; critical compo-nents; performance
Online: 8 June 2023 (03:21:11 CEST)
Redundancy in ship systems is provided to ensure operational resilience through equipment backups which ensures system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems, hence is provided with good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired black outs are common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensure a more robust maintenance and support platform for efficient ship operations. In this regard hybrid reliability and fault detection analysis were conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of 4 marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate overall low system reliability of less than 70 per cent within the first 24 months of the 78 operational months. Component criticality using reliability importance measures was used to identify all components with more than 40 per cent contribution to sub systems failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Networks classification. The ANN has identified failure patten threshold at about 200kva which can be attributed to overheating, hence establishing link between component failure and generator performance.
ARTICLE | doi:10.20944/preprints202211.0316.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: Geodiversity; geosites; springs; Las Loras UNESCO Global Geopark (UGGp); hydrogeology; Ubierna Fault
Online: 17 November 2022 (01:08:40 CET)
Las Loras UNESCO Global Geopark (UGGp) is geologically diverse, particularly in relation to water-derived features: springs, karst springs, travertine buildings, waterfalls, caves. In this work, the interactions between geology, geomorphology, structures and hydrogeology are analyzed. These four components are the fundamentals of geodiversity and their interactions provide a conceptual model of hydrogeological functioning at Las Loras UGGp. The most plausible hypothesis is that the system is formed by two superimposed aquifer systems, separated by an aquitard formed by Lower Cretaceous material. The deep lower aquifer formed by the Jurassic limestones only outcrops on the northern and southern edges of the Geopark and in a small arched band to the south of Aguilar de Campoo. It forms a basement subject to intense deformation. The upper aquifer system, formed by outcropping materials from the Upper Cretaceous, is a free aquifer. It is formed by a multilayered aquifer system that is highly compartmentalized by the very disturbed geomorphology of the landscape, constituting each moorland and each lora, an individualized recharge-discharge system. This model explains the base level of the rivers, the abundant number of existing springs and the permanent nature of some rivers, providing the keys to understand the bases of the geoconservation of a rich geological heritage linked to the active processes of the water cycle.
ARTICLE | doi:10.20944/preprints202011.0571.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: 1D-CNN; fault diagnosis; rolling bearing; vibration signal; single load; different load
Online: 23 November 2020 (09:22:43 CET)
The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.
ARTICLE | doi:10.20944/preprints202002.0392.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: power converter; fault diagnosis; intelligent algorithm; variational mode decomposition; deep belief network
Online: 26 February 2020 (11:25:27 CET)
The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.
ARTICLE | doi:10.20944/preprints201811.0083.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: landslide scars; canyon; fault escarpments; contourite deposits; digital elevation model; continental slope
Online: 5 November 2018 (04:06:37 CET)
The acquisition of high resolution morpho-bathymetric data on the Calabro Tyrrhenian continental margin (Southern Italy) enabled us to identify several mass-wasting processes, including shallow gullies, shelf-indenting canyons and landslides. In particular, we focus our attention on submarine landslides occurring from the coast down to -1700 m, with mobilized volumes ranging from some hundreds up to tens of millions of cubic meters. These landslides also show a large variability of geomorphic features in the headwall, translational and toe domain. Based on their morphology and distribution, four main classes of coastal/submarine landslides have been recognized: a) rocky coastal/shallow-water failures characterized by large hummocky deposits offshore; b) large-size and isolated scars with associated landslide deposits, mostly occurring on open slope environment and lower part of tectonically-controlled escarpments; c) a linear array of coalescent and nested landslide scars occurring in the upper part of tectonically-controlled escarpments and canyon flanks; d) a cauliflower array of small and coalescent scars occurring in canyon headwall. The latter two classes of landslides are also characterized by a marked retrogressive evolution and their landslide deposits are generally not recognizable on the morpho-bathymetric data. By integrating the morpho-bathymetric dataset with the results of previous studies, we also discuss the main factors controlling the variability in size and morphology of these submarine landslides to provide insights on their failure and post-failure behavior.
ARTICLE | doi:10.20944/preprints201808.0130.v1
Subject: Engineering, Mechanical Engineering Keywords: SHM; Electromechanical Impedance; Piezoelectricity; Intelligent Fault Diagnosis; Machine Learning; CNN; Deep Learning
Online: 6 August 2018 (21:51:53 CEST)
Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.
ARTICLE | doi:10.20944/preprints201807.0576.v1
Subject: Engineering, Mechanical Engineering Keywords: Ball bearing; Fractional Lorenz chaos system; Extension theory; Chua’s Circuit Fault diagnosis
Online: 30 July 2018 (10:00:57 CEST)
In this study we used a non-autonomous Chua’s Circuit, and the fractional Lorenz chaos system together with a detection method from Extension theory to analyze the voltage signals. The measured bearing signals by acceleration sensor were introduced into the master and slave systems through a Chua’s Circuit. In a chaotic system minor differences can cause significant changes that generate dynamic errors, and extension matter-element models can be used to judge the bearing conditions. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from Discrete Fourier Transform analysis, Wavelet analysis and an integer order chaos system. The diagnostic ratio showed the fractional order master and slave chaos system calculations. The results show that the method presented in this paper is very suitable for monitoring the operational state of ball bearing system to be superior to the other methods. The diagnosis ratio was better and there were other significant advantages such as low cost and few.
ARTICLE | doi:10.20944/preprints201804.0092.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: fractures; potential fields; lineament analysis; fault reactivation; Midcontinent rift system; Nemaha uplift
Online: 8 April 2018 (10:52:09 CEST)
Reactivation of pre-existing weaknesses in the upper crust can be documented using surface features, and has occurred throughout time and space, particularly in regions where the basement material dates from the Precambrian and has undergone successive deformation events. This study aims to use surface features such as fracture patterns to document evidence of such reactivation in the Paleozoic and Cenozoic of Nebraska and Kansas (units separated by an unconformity in the study area). The most prominent basement features in southeast Nebraska and northeast Kansas are oriented NE-SW, likely related to the midcontinent rift, and oriented NW-SE, likely related to fabrics from the Central Plains Orogen. These features are well defined in the potential fields data. Fracture patterns in the study area show an E-W oriented trend, as well as clearly discernable NE-SW and subsidiary N-S and NW-SE trends. The E-W trend is interpreted to be related to far-field stresses from Laramide and Ancestral Rocky Mountain orogenic events, whilst the NE-SW trend is interpreted to be related to subtle reactivation on the Mid-continent rift and related faults, observed in basement data. These movements produced stresses of sufficient magnitude to produce extensional fractures in the overlying rock units, but not sufficient to generate shear. Similarly, the ~N-S and NW-SE fracture trends are taken as evidence of subtle reactivation on the Nemaha Uplift and Central Plains Orogen systems, generating fractures but not shear movement. This contribution therefore provides a convincing case-study of the value of fracture orientations (that is, surface morphodynamics) in discerning buried tectonic trends and subtle reactivation thereon.
ARTICLE | doi:10.20944/preprints201904.0294.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: distributed generator (DG); medium voltage direct current (MVDC) system; voltage sourced converter (VSC); fault ride-though (FRT); trigger type superconducting fault current limiter (SFCL); active power tracking control (APTC)
Online: 26 April 2019 (10:03:30 CEST)
Building a new power plant to address the growing demand for power due to population concentration in the metropolitan area is one of the world's major concerns. However, since a large power plant can not be located around the city due to burden of economic cost, building power plant outside metropolitan and cities is necessary. Therefore, new power generation facilities are promoting policies to provide distributed generator(DG) with a small capacity relatively near the metropolitan. When the DG (photovoltaic, wind farm, etc.) is connected with the grid using medium voltage direct current (MVDC) system, voltage sourced converter(VSC) should supply reactive power to the grid, because of fault ride through(FRT) operation in grid fault. If the voltage drop is severe, the converter should be disconnected from the grid immediately without supplying the reactive power, resulting in a considerable economic loss. In general, superconducting fault current limiter(SFCL) is introduced as a measure to enhance FRT capability. In this paper, we use trigger type SFCL which protects superconducting element and reduces low voltage. On the other hand, the active power unbalance of the DC-link and the DC voltage rise due to the reactive power supply of the grid-side converter. The rise of the DC voltage causes the P (active power), Q (reactive power) control of the converter to deviate, causing malfunction and damage of the DC equipment. Therefore, the rise of the DC voltage must be prevented. In this paper, we consider the suppression the DC voltage rising caused by the FRT operation through the active power tracking control (APTC).
ARTICLE | doi:10.20944/preprints202307.0949.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: Fault detection; reservoir characterization; seismic images; deep learning; convolutional neural network; variance attribute
Online: 14 July 2023 (12:41:00 CEST)
Fault detection is an important step for subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complicated faulting structures of seismic images, manual seismic interpretation is time taking and need intensive work. To overcome this problem, geoscientists are coming up with productive computer-aided techniques for assisting in interpreter science for many years. However, in this paper, we used a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field of Browse Basin, Australia. Basically, this field is highly structured with complex normal faulting throughout the targeted Plover Formations. So, our motivation for this work in this field is to compare machine learning-based fault prediction to user interpreted faults identification with supported by seismic variance attribute. We found very satisfying result using DL with having an improved fault probability volume that outperform variance technology. Therefore, we propose that this workflow could reduce time and able to predict fault quite accurately in any field area.
ARTICLE | doi:10.20944/preprints202304.0378.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: Crustal deformation; Haiyuan-Liupanshan fault zone; Slip rate; Locking depth; Slip deficit rate
Online: 17 April 2023 (03:08:46 CEST)
Northeastern margin is a natural experimental field for studying crustal extrusion and expansion mechanism. Accurate crustal deformation pattern is a key point in the analysis of regional deformation mechanism and seismic hazard research and judgment. In this paper, the present-day GPS velocity field on the northeastern margin of the Tibetan Plateau was obtained from encrypted GPS observations around the Haiyuan-Liupanshan fault zone, combined with GPS observations on the northeastern margin of the Tibetan Plateau from 2010 to 2020. Firstly, we divided the study area into three relatively independent blocks: ORDOS block, Alxa block and Lanzhou block; secondly, the accurate fault distribution of the Haiyuan-Liupanshan fault zone was taken into account to obtain the optimal inversion model; finally, using the block and fault back-slip dislocation model, the inversion got the slip rate distribution, locking depth and slip deficit rate of each fault. The results indicate that the Laohushan Fault and Haiyuan Fault are dominated by left-lateral strike-slip, while the Liupanshan Fault is dominated by thrust dip-slip, and the Guguan-Baoji Fault has both left-lateral strike-slip and thrust dip-slip components. The maximum locking depths of the Laohushan Fault, Haiyuan Fault, Liupanshan Fault and Guguan-Baoji Fault are 5 km, 13 km, 15 km and 10 km respectively, and the locking of Haiyuan Fault is strong in the middle section and weak in the eastern and western section. The Haiyuan Fault is still in the post-earthquake stress adjustment stage. The slip deficit rate decays from 3.6 mm/yr to 1.8 mm/yr from west to east along the fault zone. Combined with geological and historical seismic data, the results suggest that the mid-long term seismic risk in the Liupanshan Fault is high.
ARTICLE | doi:10.20944/preprints202211.0094.v1
Subject: Engineering, Mechanical Engineering Keywords: Bearing fault feature extraction; Blind deconvolution (BD); Multi-task optimization; Convolutional neural network
Online: 4 November 2022 (13:41:46 CET)
Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.
ARTICLE | doi:10.20944/preprints202108.0501.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Automotive industry; Bayesian network; Fault tree analysis; Fuzzy set theory; Maintenance optimization; Uncertainty
Online: 26 August 2021 (09:49:42 CEST)
Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.
ARTICLE | doi:10.20944/preprints202107.0476.v1
Subject: Engineering, Automotive Engineering Keywords: Battery Energy Storage System; Crowbar; Fault Ride Through Capability; Vector control; Wind turbine
Online: 21 July 2021 (09:51:48 CEST)
Doubly Fed Induction Generator (DFIG) has a stator winding directly coupled with grid. Whereas, rotor winding is connected via a fault-prone back to back power converters. DFIG is known to be vulnerable to the grid faults. In early times, when a fault occurred, these generators were required to disconnect from the grid to secure the generator and power converters. However, due to the increased penetration of wind turbines into the power system, grid operators demanded that the wind turbines remain connected to the grid, as disconnecting them would further disrupt the grid. When a fault at the grid terminal occur, a high stator current is induced which further result in high rotor current. This current will trigger the DC-link voltage to rise. This high currents and DC-link voltage will cause harm to the converters. Thus, in this paper work, the crowbar protection system is employed for protecting the converters against excess energy. Furthermore, the analysis of DFIG is rendered by integrating the crowbar protection with the Battery Energy Storage System (BESS) for a much effective outcome in enhancing machine to drive the fault. MATLAB-Simulink software is used for modeling and simulation. All system parameters are obtained from ADAMA-II Wind Farm.
ARTICLE | doi:10.20944/preprints202106.0484.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: 2018 Palu earthquake, pull-apart basin, basin-shortcut fault, lateral spreading, optical correlation
Online: 18 June 2021 (15:08:26 CEST)
Our understanding of pull-apart basins and their fault systems has been enhanced by analog experiments and simulations. However, there has been no opportunity to compare the faults that constitute pull-apart basins with surface ruptures during earthquakes. In this study, we investigated the effects of a 2018 earthquake (Mw 7.5) on a pull-apart basin in the Palu-Koro fault system, Sulawesi Island, Indonesia, using geomorphic observations in digital elevation models, optical correlation with pre- and post-earthquake satellite images. A comparison of active fault traces determined by geomorphology with the locations of surface ruptures from the 2018 earthquake shows that some of the boundary faults of the basin are inactive and that active faulting has shifted to basin-shortcut faults and relay ramps. We also report evidence of lateral spreading, in which alluvial fan materials moved around the end of the alluvial fan. These phenomena may provide insights for anticipating the location of future surface ruptures in pull-apart basins.
ARTICLE | doi:10.20944/preprints201806.0189.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Doubly Fed Induction Generator; Low Voltage Ride Through, Voltage Sags, Fault Ride Through
Online: 12 June 2018 (10:56:51 CEST)
The paper presents dynamic and transient behaviour of the Doubly Fed Induction Generator (DFIG) in the wind farms. When Voltage sag or any faults occurs in the network, the variables in the doubly Fed Induction Generators are varying severely. If the voltage sag occurs, Active and Reactive power that generated by the DFIG will decrease and will increase respectively, The DC voltage link will be bigger, and the rotor current will increase. The DFIG in the wind farms uses Proportional Integral controller for controlling of electronic devices (Rotor Side Converter and Grid Side Converter). Even though the model of Doubly Fed Induction Generators and electronic device in the paper are linear, The Proportional Integral controllers cannot protect and control the variables. So, the power electronic converters and the DC-link can damage due to over voltages and over currents. Doubly Fed Induction Generator in the wind farm is simulated in MATLAB software. The results of the simulation present over voltage and over current in the DC-link and in the rotor of the Doubly Fed Induction Generator. In addition, it will explore the effect of Proportional Integral controller in Doubly Fed Induction Generator when three-phase short circuit fault occurs
ARTICLE | doi:10.20944/preprints202210.0004.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Electrical Power Grids; Fault Forecasting; Long Short-Term Memory; Time Series Forecasting; Wavelet Transform
Online: 3 October 2022 (10:36:14 CEST)
The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.
ARTICLE | doi:10.20944/preprints202201.0249.v1
Subject: Computer Science And Mathematics, Applied Mathematics Keywords: domination, detection system, identifying-code, open-locating-dominating set, fault-tolerant, king's grid, density
Online: 18 January 2022 (09:43:55 CET)
A detection system, modeled in a graph, uses "detectors" on a subset of vertices to uniquely identify an "intruder" at any vertex. We consider two types of detection systems: open-locating-dominating (OLD) sets and identifying codes (ICs). An OLD set gives each vertex a unique, non-empty open neighborhood of detectors, while an IC provides a unique, non-empty closed neighborhood of detectors. We explore their fault-tolerant variants: redundant OLD (RED:OLD) sets and redundant ICs (RED:ICs), which ensure that removing/disabling at most one detector guarantees the properties of OLD sets and ICs, respectively. This paper focuses on constructing optimal RED:OLD sets and RED:ICs on the infinite king's grid, and presents the proof for the bounds on their minimum densities; [3/10, 1/3] for RED:OLD sets and [3/11, 1/3] for RED:ICs.
ARTICLE | doi:10.20944/preprints202111.0006.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: Digital Twin; Blockchain; Proof-of-Work; Microservices; Singular Spectrum Analysis (SSA); Byzantine Fault Tolerance
Online: 1 November 2021 (11:21:41 CET)
Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain assures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner-Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners’ running status. By timely monitoring miner’s footage that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA) based detection achieves to identify individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect byzantine miners that collude to compromise a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of proposed MinT scheme under a distributed byzantine network environment.
ARTICLE | doi:10.20944/preprints202106.0633.v1
Subject: Engineering, Automotive Engineering Keywords: Conditional temporal moments; Optimizable support vector machine (SVM); Gearbox fault diagnosis; Vibration analysis.
Online: 28 June 2021 (09:57:04 CEST)
Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.
ARTICLE | doi:10.20944/preprints202105.0315.v1
Subject: Engineering, Automotive Engineering Keywords: fuzzy systems; neural networks; fault diagnosis; data--driven approaces; robustness and reliability; wind turbine
Online: 13 May 2021 (17:39:44 CEST)
The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. In fact, the prompt detection and the reliable diagnosis of faults in their earlier occurrence represent the key point especially for offshore installations. For these plants, operation and maintenance procedures in harsh environments would inevitably increase the cost of the energy production. Therefore, this work investigates fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis strategies that exploit the estimation of the fault by means of data--driven approaches. This solution leads to the development of effective methods allowing the management of partially unknown information of the system dynamics, while coping with measurement errors, the model--reality mismatch and other disturbance effects. In mode detail, the proposed data--driven methodologies exploit fuzzy systems and neural networks in order to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are estimated from the input and output data measurement acquired from the considered dynamic process, the capabilities of their fault diagnosis capabilities are validated by using a high--fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches.
ARTICLE | doi:10.20944/preprints202104.0499.v1
Subject: Engineering, Automotive Engineering Keywords: fault detection; induced draft fan; multivariate state estimation technique (MSET); model update; power plant
Online: 19 April 2021 (14:36:56 CEST)
The induced draft (ID) fan is important auxiliary equipment in the thermal power plant. It is of great significance to monitor the operation of the ID fan for safe and efficient production. In this paper, an adaptive warning model is proposed to detect early faults of ID fans. First, a non-parametric monitoring model is constructed to describe the normal operation states with the multivariate state estimation technique (MSET). Then, an early warning approach is presented to identify abnormal behaviors based on the results of the MSET model. As the performance of the MSET model is heavily influenced by the normal operation data in the historic memory matrix, an adaptive strategy is proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the model. The proposed method is applied to a 300 MW coal-fired power plant for early fault detection, and it is compared with the model without an update. Results show that the proposed method can detect the fault earlier and more accurately.
ARTICLE | doi:10.20944/preprints202101.0632.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: PV plants; Self-Organizing Maps; Fault Prediction; Inverter Module; Key Performance Indicator; Lost Production
Online: 29 January 2021 (15:42:39 CET)
In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
REVIEW | doi:10.20944/preprints201912.0072.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: Sporadic tasks; fault tolerance; scheduling; real time system; virtualized clouding; petri net; distributive systems
Online: 5 December 2019 (11:50:40 CET)
Scheduling of real time tasks are very important aspect in systems as processes should complete its task at a specific time. There is a need of high energy efficiency and low response time in large data stream so for this energy efficient resources and optimized frameworks are needed. Both hard real time and mixed critically systems are targeted. Soft deadline can be handled while hard deadlines are difficult to cater. Different algorithms are used to schedule tasks like rate monotonic, earliest deadline first, deadline monotonic etc.
ARTICLE | doi:10.20944/preprints201807.0206.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: storage and retrieval processes; load-balancing; fault tolerance; energy efficiency; memory efficiency; data loss
Online: 11 July 2018 (14:47:31 CEST)
Load balancing, energy efficiency and fault tolerance are among the most important data dissemination issues in Wireless Sensor Networks (WSNs). In order to successfully cope with the mentioned issues, two main approaches (namely, Data-centric Storage and Distributed Data Storage) have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the mentioned problems, the proposed solutions typically focus on one issue at a time. In this paper, we integrate the Data-centric Storage (DCS) features into Distributed Data Storage (DDS) mechanisms and present a novel approach, denoted as Collaborative Memory and Energy Management (CoMEM), to overcome both problems and bring memory and energy efficiency to the data loss mechanism of WSNs. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms existing approaches in various WSN scenarios.
ARTICLE | doi:10.20944/preprints202308.0754.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: condition-based diagnosis; fault locating; frequency domain reflectometry (FDR); insulation degradation; rotating machines; stator winding
Online: 9 August 2023 (10:32:46 CEST)
Detecting, especially locating local degradations at an incipient stage, is very important for mission-critical high voltage rotating machines. One particular challenge of the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially made defects. These defects include turn-to-turn short, surface creepage, loose coils, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that the FDR can identify and locate both structure and insulation degradations of both shielded and unshielded objects with good sensitivity. This makes the FDR a comprehensive tool for fault diagnosis and aging assessment.
ARTICLE | doi:10.20944/preprints202208.0283.v1
Subject: Engineering, Industrial And Manufacturing Engineering Keywords: grinding; multivariate statistics; maintenance decision; condition-based maintenance; condition monitoring; health management; prognostics; fault diagnosis
Online: 16 August 2022 (09:44:46 CEST)
Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predicting the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.
ARTICLE | doi:10.20944/preprints202205.0244.v1
Subject: Engineering, Automotive Engineering Keywords: failure mode and effect analysis (FMEA); model-based design; automatic generation tool; fault injection simulation
Online: 18 May 2022 (12:40:58 CEST)
In the development of the safety-critical systems, it is important to perform Failure Modes and Effects Analysis (FMEA) process to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an Automatic FMEA tool which integrates the Model-based Design (MBD), FMEA, and Simulated Fault Injection techniques in a single environment. The Automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool. First, the Automatic FMEA tool automatically generates FMEA reports compared to the traditional spreadsheet-based FMEA tools. Second, the Automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the Automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the Automatic FMEA were compared to that of the legacy FMEA.
ARTICLE | doi:10.20944/preprints202010.0471.v1
Subject: Engineering, Automotive Engineering Keywords: Battery energy storage system (BESS); method of fault positions; renewable energy; transient stability; voltage sags
Online: 23 October 2020 (08:49:46 CEST)
Voltage sags can cause the interruption of power supply and can negatively affect operations of customers. In this paper, the authors study the impact of battery energy storage systems (BESS) on voltage sags. A stochastic method of fault positions is used. Faults of various types are simulated and voltages are recorded. Firstly, with the BESS integrated into the network, there are higher residual voltages, fewer voltage sags and less expected critical voltage loss. Secondly, if the BESS converter power factor is reduced, recorded residual voltages are higher, voltage sags are fewer, and the number of expected critical voltage sags is lower. Finally, when three BESS converter control modes, namely constant voltage, constant power factor, and constant reactive power, were assessed, results showed similar voltage sag performances for constant power factor and constant reactive power modes. Furthermore, operating in constant voltage control outperformed the other two modes as it resulted in higher residual voltages, a lower number of voltage sags, and fewer expected critical voltage sags. The paper has demonstrated that the BESS can improve voltage sag performance. In addition, the power factor of the BESS converter and the mode of operation of the converter can influence the magnitude of the voltage sag performance improvement.
Subject: Engineering, Electrical And Electronic Engineering Keywords: Lockstep; Reliability; Fault Tolerance; Soft Error Mitigation; Zynq APSoC; ARM Cortex-A Processor; MicroBlaze Processor
Online: 8 August 2020 (04:57:57 CEST)
An attractive option for realizing applications in radiation environments is to employ All-Programmable System-on-Chips (APSoCs) thanks to their high-performance computing and power efficiency merits. Despite APSoC's advantages, like any other electronic device, they are prone to radiation effects. Processors found in APSoCs must, therefore, be adequately hardened against ionizing-radiation to become a viable alternative for harsh environments. This paper proposes a novel triple-core lockstep (TCLS) approach to secure the Xilinx Zynq-7000 APSoC dual-core ARM Cortex-A9 processor against radiation-induced soft errors by coupling it with a MicroBlaze TMR subsystem in Zynq's programmable logic (PL) layer. The proposed strategy uses software-level checkpointing principles along with roll-back and roll-forward mechanisms (i.e. software redundancy), and hardware-level processor replication as well as checker circuits (i.e. hardware redundancy). Results of fault injection experiments show that the proposed solution achieved high soft error security by mitigating about 99% of bit-flips injected into both ARM cores' register data.
ARTICLE | doi:10.20944/preprints201908.0039.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: fault diagnosis; induction motors; wind energy generation; fourier transforms; spectral 16 analysis; spectrogram; transient regime
Online: 5 August 2019 (03:56:26 CEST)
Induction machines drive many industrial processes, and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, etc. In these cases an analysis in the time-frequency domain -such as a spectrogram- is required for detecting faults signatures. The spectrogram is built using the short frequency Fourier transform, but its resolution depends critically on the time window used to generate it: short windows provide good time resolution, but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper, this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms, and combines them into a single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.
ARTICLE | doi:10.20944/preprints201905.0199.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: wide area protection system (WAPS); reliability; Fault Tree Analysis (FTA) model; information flow; multi-service
Online: 16 May 2019 (10:10:54 CEST)
Based on the topology of wide area protection system (WAPS), after studying the reliability of hardware system and information flow in the WAPS and establishing the reliability assessment model, the multi-service reliability analysis method with multi monitoring and protection tasks in WAPS was proposed. In the model, the impact of network quality of service (QoS) such as information flow loss and delay, is studied. On the base of the model, the multi-service reliability evaluation method is employed to analyze the reliability of a WAPS of IEEE14 node power system, and the key nodes of the WAPS is given, which provides a basis for improving the reliability of the WAPS.
ARTICLE | doi:10.20944/preprints201701.0091.v1
Subject: Engineering, Mechanical Engineering Keywords: fault diagnosis; shock pulse index; maximum correlated kurtosis deconvolution; teager energy operator; rolling element bearings
Online: 20 January 2017 (04:12:25 CET)
Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.
ARTICLE | doi:10.20944/preprints202307.0909.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: contact pair; on-load tap changer; Hilbert-Huang transform; support vector machine; electromechanical combination; fault diagnosis
Online: 13 July 2023 (10:54:56 CEST)
To address the problem of limited fault diagnosis accuracy in oil-immersed on-load tap changers (OLTC), this paper introduces a novel fault diagnosis method that combines the analysis of mechanical vibration signals and high-frequency current signals. The proposed approach aims to enhance the accuracy of fault diagnosis. To begin, an experimental platform is constructed to simulate the OLTC contact. Mechanical vibration signals and high-frequency current signals are collected from the testing platform under various operating states. These signals are then subjected to wavelet packet transform for denoising, followed by correlation analysis to capture the relationship between signals in different states. Based on these preliminary steps, the features of the signals are extracted and analyzed using ensemble empirical mode decomposition and the Hilbert-Huang transform. Subsequently, a support vector machine (SVM) is employed to analyze both the mechanical vibration signal and high-frequency current signal, enabling the classification of the OLTC contact state. The results demonstrate that the combination of characteristic analysis of mechanical vibration signals and high-frequency current signals provides a more comprehensive reflection of the actual OLTC contact state under different conditions. Through the SVM classification, the error between the predicted values and real values of the two types of signals remains below 10%, validating the efficiency and feasibility of the proposed method for fault diagnosis of OLTC contacts. These research findings serve as a reliable foundation for the diagnosis of operational states of on-site oil-immersed on-load tap changers, offering valuable insights for practical applications.
ARTICLE | doi:10.20944/preprints202305.0446.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: Mongolia; Tanan sag; Fault basin; Lithologic reservoir; Fracturing; Hydrocarbon accumulation mode; Distribution of the oil reservoir
Online: 8 May 2023 (04:49:41 CEST)
According to the structural features, sedimentary evolution, types of sedimentary facies, source rocks features, diagenesis evolution, reservoir features and hydrocarbon formation mechanism, exploration status and hydrocargon resource potential, this article analyzed the major control fac-tors and hydrocarbon distribution rules of the lithologic reservoirs in continental fault basin. The research shows that three major control factors and one coupled factor (fractures act as a tie) in-fluence hydrocarbon formation of lithologic traps in Nantun Formation of Tanan Sag, which in-clude sand body types, effective source rocks and effective reservoirs. With the increase in depth, enough hydrocarbon generate in source rock under thermal evolution. Hydrocarbon generation and expulsion are more intensive when it comes to the depth threshold and critical conditions of hydrocarbon supplying are met. Traps that surrounded or contacted by source rock, or communi-cated by fault are qualified to form reservoirs. As buried depth increases, hydrocarbon genera-tion-expulsion intensity grows and the trap is more petroliferous. Hydrocarbon accumulation and reservoir formation are also controlled by sandbody accumulation conditions. When it meets the critical conditions of hydrocarbon generation and concrete oil and gas are charged in, better phys-ical properties of sandbody always indicate more hydrocarbon accumulation in the trap. Alloca-tion of sand type, effective source rock and effective reservoir is optimized under the effect of fractures, and coupled hydrocarbon reservoir with these three elements.
ARTICLE | doi:10.20944/preprints202107.0498.v1
Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Fault tolerant control; Adaptive fuzzy control; Time-varying actuator faults; Sliding mode control; Sliding mode observer
Online: 21 July 2021 (15:14:06 CEST)
In this paper, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.
ARTICLE | doi:10.20944/preprints202008.0254.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: feature selection; k-means; silhouette measure; clustering; big data; fault classification; sensor data; time-series data
Online: 11 August 2020 (06:26:43 CEST)
Feature selection is a crucial step to overcome the curse of dimensionality problem in data mining. This work proposes Recursive k-means Silhouette Elimination (RkSE) as a new unsupervised feature selection algorithm to reduce dimensionality in univariate and multivariate time-series datasets. Where k-means clustering is applied recursively to select the cluster representative features, following a unique application of silhouette measure for each cluster and a user-defined threshold as the feature selection or elimination criteria. The proposed method is evaluated on a hydraulic test rig, multi sensor readings in two different fashions: (1) Reduce the dimensionality in a multivariate classification problem using various classifiers of different functionalities. (2) Classification of univariate data in a sliding window scenario, where RkSE is used as a window compression method, to reduce the window dimensionality by selecting the best time points in a sliding window. Moreover, the results are validated using 10-fold cross validation technique. As well as, compared to the results when the classification is pulled directly with no feature selection applied. Additionally, a new taxonomy for k-means based feature selection methods is proposed. The experimental results and observations in the two comprehensive experiments demonstrated in this work reveal the capabilities and accuracy of the proposed method.
ARTICLE | doi:10.20944/preprints201805.0248.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: software fault prediction; data preprocessing; feature selection; rough set theory; class imbalance; noise filter; easy ensemble
Online: 17 May 2018 (13:01:51 CEST)
Software fault prediction is the very consequent research topic for software quality assurance. Data driven approaches provide robust mechanisms to deal with software fault prediction. However, the prediction performance of the model highly depends on the quality of dataset. Many software datasets suffers from the problem of class imbalance. In this regard, under-sampling is a popular data pre-processing method in dealing with class imbalance problem, Easy Ensemble (EE) present a robust approach to achieve a high classification rate and address the biasness towards majority class samples. However, imbalance class is not the only issue that harms performance of classifiers. Some noisy examples and irrelevant features may additionally reduce the rate of predictive accuracy of the classifier. In this paper, we proposed two-stage data pre-processing which incorporates feature selection and a new Rough set Easy Ensemble scheme. In feature selection stage, we eliminate the irrelevant features by feature ranking algorithm. In the second stage of a new Rough set Easy Ensemble by incorporating Rough K nearest neighbor rule filter (RK) afore executing Easy Ensemble (EE), named RKEE for short. RK can remove noisy examples from both minority and majority class. Experimental evaluation on real-world software projects, such as NASA and Eclipse dataset, is performed in order to demonstrate the effectiveness of our proposed approach. Furthermore, this paper comprehensively investigates the influencing factor in our approach. Such as, the impact of Rough set theory on noise-filter, the relationship between model performance and imbalance ratio etc. comprehensive experiments indicate that the proposed approach shows outstanding performance with significance in terms of area-under-the-curve (AUC).
ARTICLE | doi:10.20944/preprints202303.0222.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Multilevel Converter; Cascaded Multilevel Converter; Decentralized control; Distributed Control; Balancing Controller; Converter Reconfi guration; Fault tolerant controller
Online: 13 March 2023 (07:58:18 CET)
In modern inverter applications, multilevel converters are used thanks to their capability of reducing the passive fi lter volumes, improving the overall THD, sharing the power sources across the several converter cells, and providing a reconfi guration ability in case of failure. However, increasing the number of cells makes more complex the control of the converter. This article proposes a decentralized control technique based on the use of elementary modular controllers, associated with each converter cell and communicating with their close neighbors to obtain the balancing of the powers they supplied. Each modular controller can be dynamically removed or added to allow reconfi guration of the converter for functional safety purpose. This method is applied to a 240V/3A 5-cells Cascaded Full-Bridge Multilevel Converter. The response of the system to load transients and cell voltage disturbances demonstrates the robustness of the proposed decentralized control method. Thanks to the use of modular controllers, the number of levels of the converter can be extended to the order N without adding complexity to its control.
ARTICLE | doi:10.20944/preprints202111.0489.v2
Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Fault Tolerance; Blockchain; Internet of Things; Edge Computing; Peer to Peer; Decentralized; Sensor Networks; Verifiable Delay Functions
Online: 10 August 2022 (15:41:43 CEST)
The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architecture that harnesses the potential of edge devices, reduces network latency, and lowers infrastructure cost to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. To address these concerns, we introduce a decentralized architecture tailored for large scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. The solution combines a blockchain consensus algorithm and verifiable random functions to ensure scalability, fault tolerance, transparency, and no single point of failure. We build on our previously presented theoretical simulations with many protocol improvements and an implementation tested in a use case related to monitoring a Slovenian cultural heritage building located in Bled, Slovenia.
ARTICLE | doi:10.20944/preprints202203.0241.v1
Subject: Engineering, Control And Systems Engineering Keywords: Adaptive Constrained Control; Barrier Lyapunov Function; Fault-Tolerant Control; Nussbaum-type function; power regulation; wind turbine benchmark
Online: 17 March 2022 (03:01:31 CET)
Motivated for improving the efficiency and reliability of wind turbine energy conversion, this paper presents an advanced control design that enhances the power regulation efficiency and re-liability. The constrained behaviour of the wind turbine is taken into account, by using the barrier Lyapunov function in the analysis of the Lyapunov direct method. This, consequently, guarantees that the generated power remains within the desired bounds to satisfy the grid power demand. Moreover, a Nussbaum-type function is utilized in the control scheme, to cope with the unpre-dictable wind speed. This eliminates the need for accurate wind speed measurement or estimation. Furthermore, via properly designed adaptive laws, a robust actuator fault-tolerant capability is integrated into the scheme, handling the model uncertainty. Numerical simulations are performed on a high-fidelity wind turbine benchmark model, under different fault scenarios, to verify the effectiveness of the developed design. Also, a Monte-Carlo analysis is exploited for the evaluation of the reliability and robustness characteristics against the model-reality mismatch, measurement errors and disturbance effects.
ARTICLE | doi:10.20944/preprints201810.0683.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: industrial wireless sensor networks (IWSNs), fault diagnosis, wavelet transform, support vector machine, Industrial Internet of Things (IIoT)
Online: 29 October 2018 (12:51:44 CET)
Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.
REVIEW | doi:10.20944/preprints201809.0538.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Wind power; Fault current limiters, Doubly-fed induction generator; Fixed speed wind turbine; Series dynamic braking resistor
Online: 27 September 2018 (10:00:12 CEST)
The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.