Engineering

Abstract: Predictive maintenance has emerged as a key strategic approach across numerous industrial sectors, particularly in the current context marked by the systematic integration of artificial intel-ligence (AI) technologies into asset management processes. Within the railway domain, maintenance plays a critical role in ensuring both operational reliability and safety, with maintenance-related activities accounting for up to 40% of the overall budget allocated across the V-cycle of rolling stock development. Given the essential contribution of rail transport to sus-tainable mobility and to the movement of goods and passengers, the demand for intelligent and efficient maintenance strategies continues to grow. However, a comprehensive predictive maintenance framework tailored to the railway sector remains lacking. This study presents an in-depth review of the principal methods employed in recent years, combining a PRISMA-guided systematic search of three major scientific databases Scopus, Web of Science, and ScienceDirect. A total of 61 full-text articles were retained and analyzed in detail. The paper synthesizes current research trends, frameworks, and algorithms, categorized into data-driven, model-based, and hybrid approaches, and examines their distribution across railway subsystems, revealing a con-centration of research effort on rolling-stock components such as wheelsets and switch machines relative to less-instrumented, comparably safety-critical assets such as traction, signaling, and door systems. Building on these insights, a conceptual hybrid predictive maintenance framework is proposed as a synthesis of the reviewed approaches. Finally, the paper discusses the key challenges identified in the literature and outlines open research questions with potential direc-tions for future work.

Abstract: Fire evacuation in complex multi-storey buildings is a dynamic task in which route safety changes depending on fire development, smoke propagation, and the spatial distribution of evacuees. Contemporary research increasingly applies artificial-intelligence methods for adaptive evacuation planning, but most of these approaches achieve adaptivity at the expense of interpretability and traceability. This is a limitation that is especially critical for systems with direct relevance to human safety. The present paper introduces a fully deterministic approach to intelligent fire evacuation that extends the hierarchical building graph model proposed by Ivanov [1] with continuous sensor-based risk assessment (temperature, smoke, CO₂, crowd density), unified through weighted fusion with hysteresis. Route evaluation uses a calibrated composite edge-cost model, complemented by a threshold-based table for adaptive node priority and multi-agent coordination through virtual load, while evacuee movement is modeled by a cellular automaton. All components of the proposed system are configurable and calibrated rather than trainable, which ensures full traceability, auditability, and compliance with fire-safety regulatory requirements. Evaluation across thirteen scenarios in four real buildings shows that the system's adaptivity stems mainly from multi-agent coordination and dynamic route recomputation, rather than from offline calibration of the graph weights. Coordination reduces the standard deviation of the maximum evacuation time by a factor of 2.7 to 4.9 relative to an uncoordinated baseline algorithm, at a mean evacuation time that is practically equivalent (within 1%) or, in the worst case, about 9% higher. The system achieves complete load balancing across exits (Cliff's delta up to 1.00) and guaranteed avoidance of fire and smoke nodes in all test scenarios, with the only exception involving boundary cases in which fire spreads faster than the sensor-classification interval, a physical detection-latency limit rather than a routing failure. These results indicate that deterministic, calibrated coordination can achieve adaptivity comparable to learning-based methods while preserving the traceability and auditability required for regulatory-compliant fire-safety deployment.

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Construction workers in Riyadh often wear personal protective equipment (PPE) in hot conditions. This exploratory study examined whether heat-related discomfort, supervisor or leadership influence, and language accessibility were associated with self-reported routine PPE use. Anonymous survey responses from 85 workers at seven construction sites were analysed alongside unlinked site-observation summaries from February to April 2026. Routine use was coded from whether PPE was part of daily work or worn when reminded. Four associations were assessed using Pearson chi-square tests, Cramer’s V, odds ratios, 95% confidence intervals and Holm-adjusted p-values. Heat-related discomfort was associated with routine use, χ²(1, N = 84) = 5.05, adjusted p = 0.025, V = 0.245; the positive direction may indicate that routine users experienced more discomfort rather than that heat improved compliance. Supervisor or leadership influence showed the strongest association, χ²(1, N = 82) = 39.03, adjusted p < 0.001, V = 0.690. Training-language and safety-instruction language accessibility were also associated with routine use (adjusted p = 0.002 and 0.004). Observation summaries recorded lower correct PPE use and more heat-attributed adjustment, but counts, measured heat exposure and worker linkage were unavailable. The findings support prospective studies using validated compliance measures, measured exposure and linked observations.

Abstract: Methane emissions from offshore installations are routinely monitored using gas detection systems that form an important component of operational safety management. Detector outputs are commonly used to support safety-critical decisions during helicopter approach, vessel transfer, inspection activities, and routine offshore operations. However, the extent to which atmospheric conditions influence the effectiveness of these safety barriers remains poorly quantified. This study presents a probabilistic modelling framework to assess methane detectability as a function of emission rate, atmospheric regime, receptor height, and sensor sensitivity, with particular focus on the implications of detection failure for offshore safety. A Gaussian plume dispersion model combined with Monte Carlo simulation was used to account for variability in wind speed and wind direction, allowing estimation of detection probability under realistic offshore conditions. Detection was evaluated at two representative receptor locations: helideck height (25 m), representative of helicopter operations, and near-surface level (~2 m), representative of vessel-based detection. Emissions were varied over a wide range (0.01–1000 g s⁻¹; approximately 0.04–3600 kg h⁻¹), while detection thresholds of 0.1, 1, and 10 ppm were used to represent different classes of methane monitoring systems. Results show that methane detectability is frequently controlled by plume geometry and atmospheric structure rather than emission magnitude alone. In the near field (< ~200 m), limited vertical dispersion results in plume–sensor misalignment, producing false-negative detection outcomes in which substantial methane releases remain undetected despite the presence of monitoring systems. Detection improves at intermediate distances (~400–800 m), where plume spreading increases the likelihood of plume interception, before decreasing again at larger distances due to dilution. Increasing detection thresholds significantly reduces detectability, with 10 ppm sensors failing to detect large emissions across wide operating conditions, particularly under stratified atmospheric regimes. These findings demonstrate that gas detection systems should be regarded as conditional safety barriers whose effectiveness depends on atmospheric transport processes as well as sensor performance. The absence of methane detection should therefore not be interpreted as evidence that a hazardous emission is absent. This has direct implications for offshore risk management, situational awareness, and safe-ty-critical operational decision-making.

Abstract: Fall from height (FFH) remain the predominant cause of fatalities and injuries in the construction in construction and industrial safety worldwide. Traditional accident cause analysis approaches often focus on single-factor identification or static statistical analysis, ignoring the hierarchical coupling and correlational relationship of multiple factors. To address this research gap, this study proposes a novel method based on overlay network chain in quotient space to identify overlapping causal factor clusters in FFH accidents. First, a causal factor interaction network is constructed based on the similarity of causes. On this basis, an overlay network chain in quotient space is established to identify overlapping causal factor clusters. The proposed method is validated using real FFH accident data. Results demonstrate that this approach effectively identifies hidden high-risk overlapping clusters that are undetectable by traditional methods. Notably, Cluster C5—comprising PPE non-compliance, inadequate supervision, unverified qualifications, and insufficient training—exhibits the highest occurrence rate (0.072) and structural vulnerability (CVI = 9.5), identifying it as the most critical risk pattern. The identified clusters are highly consistent with practical engineering experience. This study provides a systematic, data-driven tool for FFH accident causation analysis, supporting targeted safety prevention and control strategies.

Abstract: Artificial intelligence (AI) is increasingly used for tool-wear prediction in computer numerical control (CNC) machining, yet a high average accuracy can conceal systematic failure in the safety-critical high-wear region, where under-prediction makes a worn tool appear healthier than it is. This paper presents a trustworthy multimodal framework that audits, mitigates, and ultimately characterises this bias on the public MATWI dataset. A fusion model combining 512 image features with 84 force, acceleration, and acoustic features attains a strong test mean absolute error (MAE) of 30.9 µm, outperforming image-only (51.4 µm) and sensor-only (68.3 µm) baselines. A signed, stratified bias audit nevertheless reveals a pronounced directional error: the model predicts the healthy region to within 21.3 µm but under-predicts the worn region by approximately 85 µm (worn-region MAE 85.5 µm), with the error deepening monotonically as wear increases. A three-stage mitigation pipeline (data-level resampling, cost-sensitive and relevance-based training, and post-hoc recalibration) reduces the worn-region error by up to 18% but cannot eliminate it, and only at a measurable cost to the well-sampled region. The residual bias is shown to be both model-independent, persisting across six regressor families with a 69–127 µm residual gap, and optimisation-independent, surviving rigorous hyperparameter search and bias-aware loss functions behind an ≈ 85 µm error floor. These results establish the bias as data-intrinsic: it arises from the sparse sampling of the high-wear regime rather than from model or training choices, and the effective remedy lies in balanced, unbiased data collection rather than further algorithmic correction. The contribution is a reusable protocol for distinguishing model-reducible from data-intrinsic bias in safety-critical regression.

Abstract: Structural durability and reliability are fundamental imperatives for the safe operation of engineering infrastructure, profoundly influencing both lifecycle asset economics and socio-environmental safety. A critical component of structural integrity engineering involves the precise spatial localization of defects, the continuous monitoring of their propagation kinetics, and the high-fidelity quantification of their impact on global structural health. To achieve this, advanced diagnostic methodologies are required to detect the earliest indicators of material degradation and track its evolution throughout the operational lifespan of the asset. Crucially, these non-destructive techniques must transcend reliance on subjective, localized visual inspections or unverified numerical models. The Acoustic Emission (AE) method represents a highly effective Non-Destructive Testing (NDT) paradigm that satisfies these requirements by performing real-time analysis of active degradation mechanisms coupled with specialized reference signal databases. This paper investigates the application of an AE-based framework integrated with a fuzzy pattern recognition database—the Identifying Gas Network Anomalies (IGNA) methodology—to localize, monitor, and classify destructive processes induced by mechanical loads. The proposed approach was deployed to evaluate the structural condition of critical gas infrastructure components fabricated from steel, cast iron, and polymeric materials. Comprehensive laboratory campaigns and long-term in-situ experimental trials validated the efficacy of the AE framework for automated and semi-automated diagnostics of gas networks under real-time load conditions. The outcomes of this research led to the successful deployment of a pattern recognition-based system that facilitates proactive failure prevention and ensures the operational security of gas network infrastructure.

Abstract: Artificial Intelligence (AI)-enabled systems are now increasingly deployed across safety-critical, mission-critical, and socio-technical domains. Existing engineering disciplines offer mature approaches to human, organisational safety and software assurance; however, AI-enabled systems can exhibit characteristics such as probabilistic behaviour, data dependency, limited explainability, and adaptation that challenge traditional assurance methods. Our research examines assurance principles across three domains that arguably dominated assurance reforms in capability development during different periods: 1) human-human organisational assurance, circa 1980 to 2000, 2) software assurance, circa 2000 to 2020, and 3) assurance of AI-enabled systems, emerging since 2020. We synthesise established literature and frameworks from each paradigm, including the NIST AI Risk Management Framework, while recognising that AI assurance remains an evolving discipline. We contribute a unified set of 20 assurance precepts for safety-critical AI-enabled capabilities. These precepts are mapped across four quadrants of assurance activities, responsibilities and critical questions, and organized within a novel Dual Assurance Spiral for AI-Enabled Capabilities (DAS4AIC). Through comparative analysis, we demonstrate how classical safety principles extend from human-dominant assurance through software-dominant assurance to the assurance of AI-enabled systems. Importantly, some human-dominant assurance precepts map more directly to AI-enabled system than through software assurance. This finding may help explain operational concerns about the erosion of accountabilities, ethical responsibility and effective human oversight when AI is introduced into safety-critical capabilities. The proposed mapping was evaluated through a face-validity workshop involving an experienced and diverse group of assurance practitioners. The workshop identified critical assurance gaps, particularly in data governance, explainability, and human-autonomy teaming. We discuss the implications for organizational governance and argue that effective governance provides the foundation for auditing, training, and validating and monitoring AI-enabled systems in safety critical operational environments. To our knowledge, this is the first study to systematically align merging AI-assurance principles with the earlier and still overlapping, human–dominant and software-dominant assurance paradigms.

Abstract: In recent years, numerous incidents and accidents have occurred in PEMEX's national pipeline system in Mexico, resulting in adverse environmental impacts and fatalities. Using an intermediate methodology for quantitative and qualitative analysis, known as consequence analysis, the risk analysis was developed based on a news article dated November 26, 2018, regarding the incident on the LPG pipeline at kilometer 2.5 of Avenida 8 de Julio, in Tlajomulco de Zuñiga, Jalisco, Mexico, caused by an illegal tap. This is because, in this event, Petroleos Mexicanos withheld information, considering it likely to compromise the actions taken, and reported only the incident. This paper analyzes the incident using a consequence analysis methodology, demonstrating how, based on the limited information available, this approach enables the analysis and evaluation of potential events. This facilitates the design and implementation of preventative controls to avoid the crucial event: an explosion followed by a fire caused by a jet fire in the pipeline. The paper identifies appropriate protective barriers and mitigation actions following the onset of the incident, as well as the environmental effects of the resulting leak, using a consequence analysis methodology, with simulations of the unwanted event in the software PHAST^â and ALOHA^â.

Abstract: Reliability assessments of subsea systems are generally performed at two different levels: structural analysis of individual components and functional analysis of the complete system based on generic failure databases. This study develops a component-to-system multi-scale framework that integrates these two levels for a subsea separation system operating at 3000 m water depth. At the component level, a Gaussian process regression (GPR) surrogate model is developed using 474 finite element simulations of a vertical gravity separator. First-order reliability method (FORM) and Monte Carlo simulation (MCS) are then applied to assess structural reliability, followed by a time-variant reliability analysis considering corrosion effects. At the system level, the structural reliability model is integrated with functional failure rates through a Bayesian network that considers five equipment items and relevant risk-influencing factors. The surrogate model accurately predicts collapse pressure with an R² value of 0.996. The intact separator achieves a reliability index of 4.55, satisfying the DNV high safety class target, with the structural failure mode contributing only 0.003% of the total separator failure frequency. Under a corrosion rate of 0.4 mm/year, the reliability index decreases to 3.12 over a 25-year service period. The system crosses the medium safety class target failure rate of 10⁻⁴ per year at year 12, increasing the structural contribution to the overall system failure frequency to 0.33%. Sensitivity analysis indicates that initial ovality and wall thickness are the most influential parameters affecting structural reliability and should therefore be prioritised in design and integrity management strategies.

Abstract: The occurrence of forest fires in China was accurately predicted to optimize resource allocation and preventive mitigation. Drawing on climate, forest resource, and socio–economic factors together with forest fire incident records from 2003 to 2023, we construct a deep learning model that integrates a Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) with a Multi-Head Attention (MHA) mechanism and a Kepler Optimization Algorithm (KOA). The CNN-GRU captures spatiotemporal features, MHA enhances the recognition of intrinsic data relationships, and KOA automatically tunes network parameters. Our KOA-CNN-GRU-MHA model surpasses traditional machine learning baselines and the native CNN-GRU model, reducing the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) by 26.75%, 64.35%, and 16.47%, respectively, for the total annual Number of Forest Fires (NFF), and by 39.07%, 76.03%, and 32.82% for the total annual Number of Small Forest Fires (NSFF). Ranking the influence of predictor variables further guides fire management strategies and supports more effective operational planning.

Abstract: Lithium-ion batteries (LIBs) electric bicycles are widely used in China, with many accidental fires occurring in parking facilities in high-rise building. Electric bicycle parking areas in high-rise buildings have become fire-prone zones. There are urgent needs to establish fire codes for the parking facilities in high-rise buildings. But only limited research has been conducted on protecting against such fires. There are also uncertainties in the appropriate methods for implementing fire barriers and fire suppression facilities. To better understand the parking facility fires in this area, four fire scenarios were studied in this paper, aiming to seek principles on how to prevent serious fire accidents by isolating E-bicycles parked in parking facilities. These principles include fire barrier design, fire separation distance between the islands and the selection of fire suppression devices. A total of six experiments on LIBs bicycle fires were conducted. Fire spread between the LIBs bicycles and the propagation patterns of smoke generated by electric bicycle fires within parking facilities were studied. The effectiveness of different fire extinguishing methods in suppressing LIBs bicycle fires was discussed. The reasonable fire separation distance for electric bicycles was determined. It was found that LIBs with ternary lithium-ion batteries (such as NCM) are more prone to initiate thermal runaway. A sprinkler system with lower hazard class is proposed to operate under lower water pressure and flow rates. Fire control methods were proposed, such as including fire-resistive eave and fire barrier. The results can be used in setting up fire code and are useful for AI training cases in developing fire models.

Abstract: Recent advances in Micro-Electro-Mechanical Systems (MEMS) have enabled the development of accelerometers increasingly suitable for seismological and structural engineering applications. Quartz MEMS (QMEMS) sensors combine low self-noise, wide dynamic range, excellent thermal stability, and compact dimensions, providing a cost-effective alternative to conventional force-balance and piezoelectric accelerometers. This study presents the development and validation of a complete QMEMS-based sensing platform for seismic monitoring and Structural Health Monitoring (SHM), integrating the recently introduced Epson M-A370 accelerometer, a Smart Sensor Box with precise timing synchronization, embedded acquisition and edge-processing capabilities, and comprehensive laboratory and field validation. The M-A370 is evaluated against the previous-generation M-A352 and representative MEMS, piezoelectric, and force-balance accelerometers. Experimental results demonstrate that accelerometer self-noise is a primary factor governing the reliability of Operational Modal Analysis (OMA) and long-term SHM. Self-noise densities below 1 μg/√Hz, and preferably below 0.5 μg/√Hz, are shown to be necessary for robust modal identification and long-term tracking of structural dynamic properties under weak ambient excitation. The ultra-low-noise M-A370 (0.02 μg/√Hz) provides data quality comparable to engineering-grade force-balance accelerometers while enabling continuous monitoring of buildings, bridges, and heritage structures. The proposed sensing platform, combining ultra-low-noise QMEMS technology, precise timing synchronization, and embedded processing, provides a scalable framework for Urban Seismic Observatories, Operational Modal Analysis, distributed SHM systems and impact-based Earthquake Early Warning.

Abstract: The quality of sweet tamarind fruit, as determined by its total soluble solids (TSS), titratable acidity (TA), and TSS/TA ratio, is important for consumer satisfaction. Nondestructive techniques are therefore required to assess the quality of sweet tamarind fruit. This study investigated whether near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm can be used as a non-destructive method to assess TSS, TA, and the TSS/TA ratio of sweet tamarind fruit and to classify it under commercial standards. NIR-HSI, combined with deep learning and chemometrics, was applied for quantification and qualification analyses. Calibration models for determining TSS, TA, and the TSS/TA ratio were developed using partial least squares regression (PLSR) and support vector machine regression (SVMR). A combination of 1st derivative and SNV spectral pretreatment, was optimized to establish an SVMR model for TSS determination. MSC spectral pretreatment was optimized to develop the SVMR model for TA assessment, and the 1st derivative spectral pretreatment was optimized to establish an SVMR model for the TSS/TA ratio. Correlation coefficients of prediction (Rp) of 0.959, 0.961, and 0.956 were obtained with root mean square errors of prediction (RMSEP) of 1.102%, 0.369%, and 11.282 for TSS, TA, and the TSS/TA ratio evaluation, respectively. Partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used for classifying sweet tamarind fruit under a commercial acidity standard ( 4%). The SVMC with SNV spectral pretreatment produced the best prediction results for distinguishing standard and off-standard sweet tamarind fruit with an 82.86% accuracy. NIR HSI can be used to non-destructively predict the quality of tamarind fruit. It can be applied for online sorting to evaluate individual sweet tamarind fruits for grading and quality control in factory environments.

Abstract: Conventional hazard and operability (HAZOP) studies remain central to process safety management, but their periodic, document-centered implementation and dependence on expert judgment limit their ability to track dynamic operational risk across the full plant lifecycle. This critical literature review examines how Digital Twin (DT) and Artificial Intelligence (AI) technologies may augment HAZOP practice, advancing an augmentation principle: DT-AI should strengthen expert-led hazard analysis as a decision-support layer, not replace human judgment. The review synthesizes representative literature on DT-AI-enabled HAZOP enhancement and maps conventional HAZOP limitations to four complementary technology pathways: AI-assisted knowledge capture and deviation reasoning; DT-based hazard monitoring and early warning; hybrid physics-data modeling for predictive safety; and explainable AI for operator trust and regulatory acceptance. The review shows that these pathways offer strong potential to improve HAZOP completeness, traceability, operational relevance, and lifecycle learning. However, most implementations remain at conceptual, prototype, or limited pilot levels, with limited evidence of long-term industrial validation. Key failure modes-including model drift, sensor faults, large language model hallucination, and automation complacency-require mitigation through validation protocols, explainability, human oversight, cybersecurity, and regulatory engagement. The review proposes a lifecycle-oriented safety-management framework and a three-horizon research agenda for advancing toward reliable HAZOP-informed Digital Twin systems.

Abstract: Adulteration of freshly milled rice with rice from older sources is a fraudulent and illegal practice that exploits consumers. The purpose of this study was to develop a rapid and non-destructive technique that can detect this adulteration of milled rice, using near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm. Adulterated samples were prepared by adding old and freshly milled rice at different levels, scanning the mixed samples, and comparing the results with 100% freshly milled rice samples. All samples were divided into a calibration set and a prediction set to establish classification and calibration models. Spectral pretreatment methods were tested to develop the optimum models. For qualitative prediction, the best results for differentiation between freshly milled rice and adulterated samples using support vector machine classification (SVMC) yielded 92.31% accuracy, a 7.69% error rate, 88.89% sensitivity, and 96.55% specificity. For quantitative prediction, the best calibration model for determining the percentage of mixing with old rice using support vector machine regression (SVMR) gave results of coefficient of determination of prediction (R2p) = 0.95, and root mean square errors of prediction (RMSEP) = 6.75%. These results indicated that NIR-HSI could be successfully used in both qualitative and quantitative analyses to detect adulteration of freshly milled rice with old rice. It can be used as a rapid, nondestructive technique for assessing the authenticity of milled rice.

Abstract: Electrochemical impedance spectroscopy (EIS), a rapid and non-destructive detection technique, offers a novel technical pathway for monitoring the degradation and assessing the quality of gear oil. This research delves into the electrochemical response characteristics and the underlying evolution mechanism of high-viscosity gear oil specifically formulated for wind turbines during its oxidative degradation process. Utilizing Mobil SHC™ Gear Oil 320 WT as the research subject, oil samples with varying degrees of degradation were prepared through accelerated oxidation experiments. Broadband frequency-sweep EIS testing was employed to acquire impedance spectra. The EIS data were subsequently analyzed using the equivalent circuit (ECM) method to extract electrochemical fingerprint parameters, enabling a systematic analysis of the variation patterns in the electrochemical response of gear oil with respect to oxidation temperature and time. Concurrently, the impact of test temperature on measurement results was evaluated. The findings reveal that EIS can dissect the intricate oxidative degradation process of gear oil into quantifiable interfacial electrochemical responses, with the characteristic parameters derived from ECM demonstrating consistent trends. Through a comprehensive analysis of the evolution patterns of electrochemical fingerprints, the oxidative degradation state of gear oil can be effectively evaluated. This research provides empirical evidence supporting the application of EIS technology in monitoring oxidative degradation and assessing the quality of gear oil for wind turbines.

Abstract: Quality risks in digitalized equipment manufacturing processes are increasingly characterized by multi-source coupling, chain-like transmission, and latent evolution, making it difficult for single-method approaches alone to fully reveal their transmission mechanisms and key driving factors. This study proposes a mixed-methods framework integrating grounded theory, covariance-based structural equation modeling (CB-SEM), and XGBoost-SHAP. First, grounded theory was applied to in-depth interview data to develop a five-dimensional quality risk framework covering process design and transformation, data flow and collaboration, production execution and process control, quality inspection and data traceability, and personnel capability matching. Second, based on 420 valid questionnaire responses, CB-SEM was used to validate the chain transmission path of risks along “process design–data collaboration–production execution–inspection and traceability” and to reveal the pervasive effect of personnel capability matching risk, with indirect effects accounting for 43.5%. Finally, the XGBoost-SHAP framework was introduced to capture nonlinear and item-level effects, identifying key risk drivers such as the absence of pre-job certification, underlying control failures, and distorted simulation boundary conditions. By integrating qualitative construction, linear validation, and nonlinear attribution, this study provides cross-method evidence for identifying the driving factors and transmission mechanisms of quality risks in equipment digital manufacturing processes. The findings deepen the understanding of how quality risks are formed and transmitted across equipment digital manufacturing processes and provide decision support for agile and targeted digital quality control in equipment manufacturing enterprises.

Abstract: The stability of financial institutions is crucial; however, current regulations evaluate credit, liquidity, and market risks separately, which hampers a consistent assessment of an entity's true loss-absorbing capacity. To address this, our study introduces the Risk Capacity Index (ICR) as a comprehensive indicator of financial sustainability for organizations in Colombia's solidarity sector. The approach adjusts a macrofinancial risk capacity model to fit the institutional setting, defining the ICR as the ratio of technical equity to total risk exposure, including expected credit losses, market Value-at-Risk, and the liquidity gap. This index was empirically tested with monthly data from 2025 from a closed savings and credit cooperative, using sensitivity tests and stress scenarios aligned with Basel III standards. Results show that liquidity risk is the main driver of capacity depletion, responsible for most of the index's fluctuations and causing non-linear deterioration during adverse conditions, while market risk effects are minor. Significant funding pressures sharply reduce the ICR below viability levels, leading to structural issues on the balance sheet. The ICR provides a new, integrated early-warning tool that complements traditional solvency measurements. The study highlights that managing liquidity and liabilities proactively, rather than just increasing capital, is key to preserving financial stability in cooperative models.

Abstract: IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally-structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at AUROC 0.93), detecting 100% of injected faults at a mean delay under 25 minutes. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW's shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW's shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems.