Abstract: Leveraging its exceptional ultra-low altitude flight capability and high economic effi-ciency, the unnamed Wing-in-Ground (WIG) craft offers unique advantages in mari-time missions such as island patrol and rapid replenishment. However, the path plan-ning for unnamed WIG crafts faces the dual challenge of precise obstacle avoidance and ultra-low altitude maintenance, due to the obstacle distribution in island regions and the altitude window constraints inherent to ground effect flight. To address this, this paper proposes a path planning method based on an improved hybrid Sparrow Search Algorithm and Grey Wolf Optimizer. This method integrates the swarm intel-ligence of the Sparrow Search Algorithm, employs a self-destruction mechanism to es-cape local optima. Furthermore, it combines the hierarchical guidance of the Grey Wolf Optimizer to enhance convergence accuracy. The algorithm integrates ground-effect maintenance constraints and a reef threat model, and smooths the final path using cubic B-spline curves. Simulation results demonstrate that the proposed algorithm outperforms the standard Sparrow Search Algorithm, Grey Wolf Optimizer, and Particle Swarm Optimization in terms of convergence speed, optimization accu-racy, and obstacle avoidance success rate. It is capable of generating a feasible, safe, and smooth path, thereby supporting the autonomous navigation of unmanned WIG crafts in island reef waters.

Abstract: Considerable progress has been made in predicting nominal NVH behavior in electric drivetrains, but the acoustic scatter observed across manufactured units remains insufficiently understood. In practice, nominally identical drive units may still exhibit noticeably different tonal behavior because small deviations in gears, shafts, bearings, fits, centering features, or assembly phase modify the excitation, transfer, and radiation mechanisms of the system. This review examines how manufacturing and assembly variability influences NVH performance in electric drive units and e-axles, with particular focus on the rotor–shaft–gear–bearing–housing system. Unlike broader EV NVH reviews, the present work focuses specifically on variability-induced acoustic scatter and its propagation along the drivetrain NVH generation and transmission path. To support transparency and consistency, the literature search and selection process followed a structured, PRISMA-inspired approach to ensure transparency and consistency across Scopus, Web of Science, Google Scholar, and SAE Mobilus for the 2015–2026 period. From 387 identified records, 50 studies were retained after duplicate removal, screening, and full-text assessment. The selected literature was synthesized into eight thematic categories: imbalance; run-out and eccentricity; bearing clearance and preload; spline and pilot centering; thermal effects; phase indexing; transmission error and sidebands; and end-of-line NVH diagnostics. The reviewed literature shows that manufacturing- and assembly-induced deviations can significantly alter transmission error, sideband structure, shaft-order content, and final tonal response, even when individual components remain within nominal tolerance limits. Beyond synthesizing the evidence base, the review proposes a general simulation methodology for variability-aware NVH prediction based on explicit deviation parameterization, hierarchical model fidelity, intermediate excitation metrics, thermal-state awareness, and closer integration with production and measurement data. Overall, the findings support a shift from nominal NVH assessment toward robustness-oriented, production-representative prediction of acoustic scatter, and establish a structured methodology for variability-aware NVH engineering in electric drivetrains.

Abstract: Pedestrian safety at urban intersections requires risk-aware mechanisms that extend beyond binary collision detection toward comparative prioritization among multiple agents. This study introduces the Intelligent Pedestrian Model (IPM). This reference-normalized scalar framework represents pedestrian risk as a function of trajectory, contextual, infra-structural, and behavioral factors, decomposed into Exposure and Severity components. Building on IPM, the Safety-Prioritized Trajectory Model (SPTM) operationalizes the Ex-posure component using an observation-only, leakage-free kinematic proxy embedded into a cost-aware negative log-likelihood objective. Evaluation on the ETH/UCY benchmark under a strictly inductive protocol shows that moderate prioritization (β ≈ 1.0) improves best-of-K multimodal performance (ALL FDE@K: 0.979 → 0.970 m) while maintaining mean displacement accuracy within seed-level variability. The results indicate that Expo-sure-based weighting does not act as a global accuracy enhancer but redistributes predictive capacity toward safety-relevant motion regimes. Validation is limited to a single benchmark fold; cross-fold generalization and full IPM instantiation remain future work.

Abstract: Progressive slope failures in open pit mining are characterized by accelerating deformations that can be monitored and potentially forecast. While current monitoring practice emphasizes velocity-based parameters and the inverse velocity method for failure prediction, the role of acceleration in understanding failure mechanisms and improving early warning systems remains underexplored. This paper presents a conceptual and analytical framework for characterizing acceleration in progressive slope failures. We introduce the concept of slope damage as a cumulative measure of positive accelerations over time, and demonstrate its utility in identifying the Onset of Acceleration (OOA), defined as the critical transition from regressive to progressive failure. We further examine the geotechnical conditions necessary for the inverse velocity method to be valid, proposing that a fully or nearly fully mobilized failure surface is required for sustained acceleration. The distinction between hazard-relevant velocity exceedance and failure-indicative progressive acceleration is discussed in the context of Trigger Action Response Plan (TARP) frameworks. This work contributes to the fundamental understanding of progressive failure mechanisms and provides practical guidance for acceleration-based slope monitoring.

Abstract: Earth-retaining Structures are engineered systems designed to hold back soil, rock, or other materials and prevent landslides or the collapse of earth onto roadways. These structures are essential for stabilizing slopes and supporting excavations. They play an important role, especially in transportation infrastructure systems. One of the biggest challenges the asset holders are facing with, is the maintenance of these important as-sets. Several techniques have been used recently to monitor the health level of these geotechnical systems. Although there are some works reviewing these structural health monitoring techniques for civil structures, none of them specifically focused on earth-retaining structures which leads to an overall lack of knowledge in this field. Therefore, this survey aims to conduct a comprehensive review of health monitoring methods that are being used for the assessment of these important geotechnical assets to highlight the current state of research, identify gaps and limitations, and suggest fu-ture directions. In particular, this paper outlines the importance of timely maintenance for earth-retaining structures, presents the types of structural health monitoring meth-ods used for predictive or preventive maintenance of these assets, and finally, identi-fies the challenges and new solutions to help with a more efficient assessment and monitoring of these assets.

Abstract: To reduce the significant environmental impact of the construction sector, developing renewable, load-bearing building materials is crucial. Straw, an abundant agricultural by-product, presents a promising resource for this purpose. This study focuses on a novel building material made from highly compressed straw, referred to as Straw-Brick, aiming to provide a reliable numerical model for predicting its complex mechanical behavior. To characterize the material, a series of uniaxial single-cycle and cyclic compression tests were conducted. Based on the experimental results, a one-dimensional, nonlinear rheological model was developed to capture the material’s key mechanical features. The model parameters were identified by fitting the simulation to the experimental data. The results show that the material behavior is characterized by strong nonlinearity, hysteresis, and stress relaxation. The proposed model is capable of capturing these main characteristics, showing good agreement with the experimental data. This work provides a validated material model that establishes a robust foundation for future numerical simulations of structural components made from this innovative and sustainable building material.

Abstract: The valorization of agricultural plant waste as a sustainable source of natural fibers has gained increasing attention due to environmental and economic concerns. This study investigates the feasibility of extracting bast fibers from Egyptian Corchorus olitorius L. (Molokhia) plant residues and evaluates the influence of different extraction methods on fiber properties. Fibers were extracted using biological retting, cold al-kaline chemical treatment (4% NaOH), and manual scraping, followed by comprehensive characterization of their morphological, chemical, crystalline, mechanical, thermal, and environmental properties. The results showed that the extraction method significantly affected fiber performance. Chemically extracted fibers exhibited the smallest average diameter (13.76 ± 0.44 μm), the highest cellulose content (72.23%), and the lowest lignin content (3.20%), indicating effective removal of amorphous components. XRD analysis revealed the highest crystallinity index for chemically extracted fibers (70.0%), compared to bi-ological (60.0%) and manual extraction (64.0%). These structural improvements resulted in superior mechanical properties, with tensile strength and Young’s modulus reaching 600.67 ± 11.73 MPa and 38.96 ± 0.64 GPa, respectively, compared to lower values for biologically and manually extracted fibers. Weight loss analysis indicated optimal extraction durations of 21 days for biological retting and 9 days for chemical treatment. ICP-MS analysis confirmed that heavy metal contents were well below Oeko-Tex® Standard 100 limits. Overall, the findings demonstrate that Molokhia plant waste is a promising and environmentally safe source of natural fibers, with cold chemical extraction offering the most effective route for producing high-quality fibers suitable for bio-based composite applications.

Abstract: Background: Accurate assessment of neonatal sleep is critical for monitoring brain development and identifying potential neurological disorders, yet manual scoring of multi-channel EEG recordings is labor-intensive and prone to variability. Methods: To address this, we propose a lightweight temporal-spatial feature fusion network for automatic neonatal sleep staging. The model employs a dual-branch architecture to separately capture temporal dependencies and spatial correlations in EEG signals, which are then integrated via an adaptive fusion module to obtain comprehensive feature representations while maintaining low computational complexity. Results: The framework was evaluated on a clinical neonatal dataset (CHFD) for tasks including sleep–wake classification, quiet sleep detection, and three-stage sleep staging, achieving superior performance compared with several state-of-the-art methods. Additional experiments on the MASS-S3 adult dataset demonstrate that the model retains competitive accuracy and F1-score, indicating strong generalization across populations. Conclusions: These results suggest that jointly modeling temporal and spatial features enables robust and efficient automatic sleep staging. The proposed approach offers a practical solution for clinical applications and edge deployment, providing reliable, multi-dimensional assessment of neonatal brain activity and laying the groundwork for future studies integrating larger datasets or multimodal physiological signals.

Abstract: Accurate detection of special targets in unmanned aerial vehicle (UAV) remote sensing imagery under complex degradation conditions remains a critical challenge for intelligent surveillance systems. Existing detectors exhibit significant performance degradation when confronted with composite degradation factors such as blur, rain, snow, fog, low illumination, strong light, and electromagnetic interference. To address this limitation, we propose RHG-DETR (Riemannian Hyper-Graph Detection Transformer), a novel detection framework for robust special target detection under multi-type degradation in UAV remote sensing imagery. Using RT-DETR as the baseline, three synergistic innovations are introduced at the backbone, neck, and encoder levels. The Dynamic Receptive-field Hyper-graph Attention Network (DRHANet) replaces the conventional ResNet backbone, employing anisotropic dynamic depthwise separable convolution and a Riemannian Hyper-graph Fusion (RHGF) mechanism to model high-order semantic topology dependencies among target components. The Bi-directional Weighted Adaptive Fusion Network (BWAFN) constructs a two-stage bidirectional feature pyramid with learnable scale contribution weights and a lightweight spatial compensation upsampler to maintain cross-scale semantic consistency under atmospheric degradation. The Adaptive Sparse Multi-scale Encoder with Dynamic normalization (ASMED) reconstructs the AIFI encoder module by introducing sparse window self-attention to suppress background interference, a spatial-gated feedforward fusion to preserve geometric topology constraints of target sub-components, and coordinated dynamic normalization modules to stabilize encoding under extreme illumination and electromagnetic interference. On a self-constructed special target dataset comprising tanks, multiple launch rocket systems, and soldiers under seven degradation types, RHG-DETR achieves an mAP50 of 78.5%, surpassing the RT-DETR baseline by 3.7%, while reducing GFLOPs and parameter count by 34.4% and 28.8%, respectively, at an inference speed of 84.2 FPS. Consistent improvements on VisDrone2019 and BDD100K further validate the cross-domain generalization capability of the proposed framework.

Abstract: Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article presents a conceptual review informed by structured scoping searches across PubMed, Scopus, Semantic Scholar, Crossref, and selected policy sources covering January 2001–March 2026. The search component was used to map the field and identify representative frameworks, implementations, and technical advances rather than to estimate pooled effects. We synthesise the literature across four domains: conceptual foundations of integrated care, AI and multimodal analytics, implementation barriers, and digital-governance requirements. On that basis, we propose a five-level taxonomy ranging from disease-specific programmes to learning integrated care models and argue that most current deployments remain concentrated at digitally integrated but only weakly adaptive Type IV configurations. Across the literature, three recurrent constraints limit progression towards Type V learning systems: temporal blind spots, maintenance debt, and governance misalignment. Overall, the review positions AI-enabled integrated care less as a finished model than as an emerging design space requiring longitudinal data assets, stewarded model lifecycles, and accountable governance to support clinically useful, equitable, and trustworthy learning systems.

Abstract: The global drive towards sustainability and energy conservation has accelerated the development of intelligent buildings utilizing building management system (BMS). Occupants have profound impacts on building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of BMS, which thus give rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environment control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control framework named MAPC, which can construct credible models with limited data and provide room-level control strategies allowing for occupant comfort and energy efficiency. Its technological innovations are twofold. On the one hand, we design a model construction and fine-tuning method combining data-driven subspace projection approach with physical priors, which can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism, which enables room-level global optimal control considering dynamic occupant comfort requirements and energy usage. Experimental results obtained on a typical duplex apartment dataset demonstrate that MAPC is able to provide room-level control strategies based on dynamic occupant requirements and user preferences, achieving improved occupant comfort and energy efficiency. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets.

Abstract: Ear biometrics has emerged as a complementary modality for biometric recognition, particularly in unconstrained environments where traditional approaches such as face recognition may be affected by pose, illumination, or occlusion. Accurate ear segmentation plays a critical role in such systems by isolating the region of interest and reducing background interference. However, reliable segmentation remains challenging under real-world conditions due to occlusions, accessories, and variations in image quality. In this work, we investigate an encoder-enhanced U-Net architecture for pixel-wise ear segmentation, incorporating a ResNet-50 backbone to improve feature representation through transfer learning. The proposed approach is evaluated on the Annotated Web Ears (AWE) dataset and the EarSegDB-25 dataset under standard experimental settings. On AWE, the model achieves a mean Intersection over Union (IoU) of 77.1% and a pixel-wise accuracy of 99.7%, outperforming previously reported encoder–decoder baselines. On EarSegDB-25, the method attains a test IoU of 94.76%, demonstrating strong segmentation performance on a dataset with diverse real-world variations. We further analyze the relationship between pixel-wise accuracy and IoU, highlighting the limitations of accuracy as a metric in background-dominated segmentation tasks. While the architectural modification is incremental, the results indicate that incorporating a pretrained residual encoder can provide consistent improvements in segmentation quality under challenging conditions. These findings support the effectiveness of encoder-enhanced U-Net models as a practical solution for ear segmentation in biometric pipelines.

Abstract: Based on outer raceway control theory and considering the effects of elastic deformation, centrifugal force, and gyroscopic moment between the rolling elements and raceways, a geometric and force analysis of angular contact ball bearings is conducted. A five-degree-of-freedom theoretical model capable of accounting for the combined action of radial force and moment is established. The accuracy of the model is verified through numerical calculations and experimental results from existing literature. Upon validation of the theoretical model, a modified Archard model is employed to develop a wear volume model for the bearing raceways. The influence of both single and combined loads on sliding wear in the bearing raceways is systematically analyzed.

Abstract:

Methane production from Brosimum alicastrum seed coat was evaluated using a logistic model through three alkaline concentrations (0.19 M, 0.26 M, and 0.28 M) and three enzymatic activity levels (3000 U mL^-1, 5000 U mL^-1, and 7000 U mL^-1) as pretreatments. Laccase was produced through submerged fermentation using T. hirsuta Bm-2 fungi, while NaOH served as the alkaline agent. Enzymatic pretreatment resulted in the highest specific CH₄ yield (427.43±2.28 mL CH₄/g VS_added), surpassing both alkaline pretreatment (235.61 ± 9.19 mL CH₄/g VS_added) and the control (102.54 ± 5.55 mL CH₄/g VS_added). Kinetic analysis of CH₄ production indicated that cumulative CH₄ production reached its stationary phase within 30 days of digestion. Moreover, enzymatic pretreatment exhibited the highest CH₄ formation rate (0.15–0.17 h^-1), except for the control, which had a slightly higher rate (0.21 h^-1). The kinetic analysis revealed that the enzymatic pretreatment significantly improved the hydrolysis stage of Ramon's seed coat, promoting higher cumulative CH₄ production and leading to an increased specific CH₄ yield.

Abstract: Hydrogen (H2) is regarded as a promising option for sustainable energy systems; however, its large-scale use in electricity supply remains limited. This study develops a stochastic network optimization model to examine the applicability of H2-based electricity generation. The proposed Hydrogen Supply Chain (HSC) model evaluates cost and emission performance under uncertainty by considering disaster conditions, transmission losses, depreciation, and the time value of money. The Marmara Region of Türkiye is divided into 24 grid nodes, and a single-period model for 2023 is solved using Mixed-Integer Linear Programming (MILP). The HSC is allowed to meet 10–40% of electricity demand and to replace collapsed grid lines by supplying critical public centers (CPCs) during disasters. The results show that the HSC can meet about 25% of demand, although at costs higher than power grid (PG) electricity, while keeping emissions near zero. The model is then extended to a multi-period structure (2023–2053) and solved by Variable Neighborhood Search (VNS). Over time, H2 costs decline, and its share rises from 19% to 35%. These findings suggest that H2 can support long-term sustainability, resilience, and energy security.

Abstract: Floating belt conveyor routes, consisting of serially arranged belt conveyors, the end parts of which are mechanically attached to floating bodies, are designed for the continuous transport of extracted granular materials from the water. The paper deals with the analytical determination of the position of the centre of gravity of the buoyancy force, the coordinates of which change depending on the longitudinal deflection of the floating body from the equilibrium state, which acts as a supporting element of individual conveyor belts. The analysis of the individual phases of deflection of the floating body, consisting of a pair of floats with a circular cross-section, shows that the complete submergence of one of the floats occurs at a higher value of the angle of inclination in the case when the floats are initially submerged under the surface to exactly half of their diameter. On the realized experimental device the buoyancy force was detected using strain gauges during the deflection of the floating body from the equilibrium position for three defined levels of immersion. The floating body of the experimental device consists of a pair of floats with a circular cross-section with a diameter of 80 mm. The output is a structured methodological procedure for determining the position of the centre of gravity of the displacement (centre of buoyancy) of a floating body when it deviates from the equilibrium position and a methodology for calculating the stability arm, which is a key parameter for assessing the buoyancy and stability of the body. On the basis of the laboratory measurements, the magnitude of the buoyancy force can be quantified as a function of the immersion depth of the floating body. It was found that the buoyancy force remains constant when the body deflects only when the immersion corresponds to half the diameter of a float with a circular cross-section. If the depth of the immersion is less than the radius of the float, the buoyancy force increases during deflection; on the contrary, if the immersion is greater than the radius of the float, the buoyancy force decreases.

Abstract: The vibroacoustic simulation of geared drivetrains has become increasingly important as electrified powertrains expose tonal gear noise and high-frequency structure-borne excitation more clearly than conventional internal-combustion vehicles. In this context, software choice is no longer a secondary implementation detail but a central engineering decision, because different platforms emphasize different parts of the excitation–transfer–radiation chain. This review therefore examines gearbox and geared-drivetrain NVH simulation from a software-specific perspective rather than a purely phenomenon-based one. The article critically compares dedicated gearbox CAE tools, general multibody dynamics platforms, integrated multiphysics and structural–acoustic finite-element environments, and early-stage 1D system simulation tools. The comparison covers major software ecosystems including KISSsoft/KISSsys, Romax Suite, SMT MASTA/DRIVA, MSC Adams, AVL EXCITE, RecurDyn/DriveTrain, Siemens Simcenter 3D Motion / Transmission Builder / Acoustics, SIMULIA Simpack, Ansys Motion with Mechanical/Acoustics and Motor-CAD, COMSOL Multiphysics, GT-SUITE, and Simcenter Amesim, while also considering relevant recent module extensions and workflow updates. The review shows that the current software landscape is structured around four main methodological layers: dedicated gearbox analysis tools that are strongest in gear-contact modeling and microgeometry iteration; high-fidelity multibody platforms that are strongest in system-level dynamic response and transmission-path representation; integrated structural–acoustic environments that provide the deepest access to housing vibration and radiated-noise prediction; and 1D or multidomain system tools that are most efficient for early concept evaluation and architecture-level trade-off studies. Recent developments since 2023 indicate a clear shift toward tighter support for electrified drivetrain NVH, measured manufacturing deviations, optimization workflows, and faster acoustic prediction, including reduced-order or embedded acoustic methods. At the same time, major gaps remain. Open literature still contains relatively few independent studies that validate the full chain from tooth contact and transmission error through dynamic transfer paths to housing vibration and radiated sound within a single commercial workflow. Likewise, interoperability for measured flank topography, wear-driven NVH evolution, and fully validated electro-magnetic–mechanical–acoustic simulation remains limited and uneven across platforms. For this reason, the review argues that current software ecosystems are best understood not as universally proven end-to-end solutions, but as partially overlapping toolchains with different strengths, evidence levels, and practical compromises.

Abstract: To address the challenge of separating fine-grained apatite from layered silicate gangue minerals (chlorite and biotite) in medium-low grade collophanite ores, this study systematically investigated the effect of carboxymethyl cellulose sodium (CMC-Na) as a selective depressant on flotation behavior of different particle size fractions and its underlying mechanism. Pure mineral and artificial mixed ore flotation experiments demonstrated that at pH 9 and collector dosage of 5 kg/t, CMC-Na enabled selective separation of apatite from gangue minerals, with optimal dosage showing significant particle size effects: for the -0.5+0.074 mm fraction, effective separation was achieved with collector alone; for the -0.074+0.023 mm fraction, the optimal CMC-Na dosage was 10~100 mg/L, yielding 87% apatite recovery for pure minerals and 41.8% recovery with 23.7% P₂O₅ grade for mixed ores; for the -0.023 mm fine fraction, the optimal dosage was 30~300 mg/L, achieving 24.8% recovery and 13.2% grade. Mechanism studies revealed that CMC-Na significantly enhanced the hydrophilicity of chlorite and biotite, enlarging their surface property differences with apatite. FTIR and XPS analyses indicated that CMC-Na adsorbed on biotite via ion exchange with interlayer K⁺ and coordination with octahedral Fe²⁺/Mg²⁺, and on chlorite through chemical coordination with octahedral Mg²⁺, whereas only weak physical adsorption occurred on apatite surface Ca²⁺. The adsorption strength followed the order: biotite > chlorite > apatite. This study provides an effective reagent scheme and theoretical basis for flotation separation of fine-grained phosphate ores.

Abstract: Accurate evaluation of indoor daylighting performance is essential for improving visual comfort and reducing lighting energy use in office buildings. However, simulation-based daylighting analysis is often too time-consuming to support rapid comparison of multiple design options in early-stage design. To address this issue, this study proposes MTL-Light, an explainable chained multi-task learning framework for fast daylighting performance prediction in typical office units. A parametric simulation dataset was constructed, and multiple representative daylighting indicators were extracted from the spatial distribution of daylight factors on the work plane. MTL-Light was then developed to jointly predict these indicators by modeling their interdependencies within a lightweight multi-task learning architecture. In addition, SHAP was employed to interpret the prediction results by quantifying the marginal contributions of geometric design variables. The results show that, compared with single-task models, MTL-Light achieves higher accuracy and more stable performance across multiple indicators, particularly for metrics sensitive to spatial distribution. Moreover, it reduces daylighting evaluation from minute-level simulation to millisecond-level inference. The interpretability analysis further indicates that room depth and window geometry dominate daylighting performance, while different indicators exhibit different sensitivities to geometric variables.

Abstract: As the era of Industry 4.0 (i.e., the fourth-generation industrial revolution) develops, machine tools in particular are becoming interconnected, forming a collaborative community in smart factories. “Smart manufacturing” is becoming the norm, in a world where intelligent machines, systems, and networks are able to exchange information between them and respond independently, with autonomy to information, with the goal to manage industrial production processes. An important challenge is the transformation of traditional machines into intelligent machines, respectively intelligent or smart spindle. The purpose of this paper is to analyze the spindle using the intelligent models in in-situ conditions. The historical evolution, recent challenges and future trends of machine tool spindles were analyzed, noting that further development would be necessary to enable sensor/AI module integration to make the spindle unit an inherent quality assurance system. This study proposes a deep learning-based approach to spindle health monitoring based on multi sensor vibration signal analysis. The two proposed AI methods is based on the analysis of acceleration and synchronous envelope vibrations by demodulating the signal based on the Hilbert transform to identify critical bearing defects and specific defects at high frequencies.