Search Results (305)

Chillers serve as the core high-energy-consuming equipment in heating, ventilation, and air conditioning (HVAC) systems. During long-term continuous operation, they tend to suffer gradual subtle degradation, with a performance deviation less than 5%. Conventional fault diagnosis methods rely on manual threshold judgment or labeled fault data, which fail to realize accurate early warning signals. In addition, existing algorithms lack multi-dimensional baseline comparisons to verify their practical engineering performance. To address these limitations, this paper proposes an unsupervised health assessment method combining an LSTM autoencoder and Euclidean distance (LSTM-AE-ED). A multi-gradient fault time-series dataset is generated via a MATLAB R2022b/Simscape mechanism model verified by both summer field measurements and refrigeration pressure-enthalpy cycles, which resolves the practical engineering challenges of scarce on-site fault samples and potential equipment damage caused by actual fault tests. The proposed model is trained solely on healthy time-series data. It extracts dynamic coupling characteristics of chillers through LSTM, constructs a dimensionless health index based on Euclidean distance in feature space, and introduces the standard deviation of health index to improve evaluation stability. Baseline comparisons with vanilla AE and single-layer LSTM are carried out. Experimental results demonstrate that the proposed method achieves an identification accuracy of 96.3% and exhibits high sensitivity to mild degradation of four typical faults, adapting to dynamic multi-working-condition scenarios. This approach requires no additional acquisition devices for derived parameters such as power consumption and COP; online assessment can be realized merely with standard temperature, pressure, and flow sensors equipped on chillers. With lightweight inference performance, it is suitable for edge monitoring terminals of chillers in data centers, providing a low-cost and practical quantitative technical scheme for predictive maintenance and hierarchical early warning signals of refrigeration equipment. Full article

DC microgrids are important platforms for renewable energy integration, energy storage interaction, and bidirectional power exchange. In these systems, multi-phase interleaved parallel DC-DC converters are widely used as key energy-router interfaces, but open-circuit faults in power devices may lead to current imbalance, waveform distortion, ripple redistribution, and system instability. To improve fault warning and diagnosis under variable operating conditions, this paper proposes a time–frequency dual-branch efficient fully convolutional network (EPFCN). The proposed model takes synchronized multi-channel voltage/current signals and their FFT-domain representations as complementary inputs. The time-domain branch extracts transient waveform features, while the FFT-domain branch captures spectral variation and harmonic-related information. An efficient channel attention (ECA) module is introduced to enhance fault-sensitive channel responses while maintaining a lightweight structure. An RT-LAB hardware-in-the-loop platform is established to construct a multi-condition diagnostic dataset covering one normal state and nine fault states. Experimental results show that the proposed EPFCN achieves high diagnostic accuracy, strong noise robustness, clear feature separability, and feasible edge-side inference performance. The proposed method provides an effective data-driven solution for online fault warning and diagnosis of multi-phase interleaved converters in DC microgrids. Full article

The operational safety and longevity of Lithium-ion Nickel Manganese Cobalt Oxide (NMC) battery packs depend on the early detection of gradual cell degradation rather than reactive fault protection. Conventional Battery Management Systems (BMS) predominantly rely on fixed threshold-based mechanisms, which are insufficient for identifying long-term abnormal trends at the individual cell level preceding failure. This studyproposes an intelligent IoT-based battery monitoring and visualization framework for trend-oriented abnormal behavior analysis in a 72 V, 20 cell NMC battery pack. A JK-BMS performs cell voltage acquisition, while an ESP32-S3 microcontroller operates as an IoT gateway, wirelessly collecting high-resolution cell level data via Bluetooth Low Energy (BLE). The data are transmitted to a Home Assistant platform, which provides centralized time-series visualization and comparative cell analytics. The primary contribution is a heuristic anomaly detection algorithm that evaluates temporal voltage trends of individual cells, with emphasis on instability within the critical operating range of 3.0–3.5 V. Unlike conventional threshold-based approaches, the proposed method detects repeated abnormal patterns over time. A frequency-based alert mechanism categorizes battery health into normal, warning, and critical states based on cumulative anomaly occurrences, enabling progressive degradation assessment. Experimental results demonstrate that the proposed framework effectively identifies early-stage degradation patterns that remain undetected by conventional BMS logic. The system supports predictive maintenance, enhances operational safety, and provides a scalable, cost-effective solution for advanced battery health monitoring in electric mobility and distributed energy storage applications. Full article

Precise monitoring of vibration signals is crucial for early fault warning and localization in industrial applications. Traditional electromagnetic accelerometers are often unsuitable for harsh environments characterized by high temperatures, high pressures, and strong electromagnetic fields. Fiber Bragg grating (FBG) accelerometers have become a major research topic in this field due to their unique advantages, including resistance to high temperature and pressure, immunity to electromagnetic interference, and ease of wavelength division multiplexing. This paper provides a systematic review of FBG accelerometers, covering their fundamental principles, classification, performance enhancement strategies, and applications. We focus on reviewing the research progress of FBG accelerometers from two main aspects, single-axis and multi-dimensional vector types, and offer an outlook on future development to provide a reference for the research and application of FBG accelerometers. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Aero-engine predictive maintenance is challenged by heterogeneous operating conditions, complex degradation patterns, and the need for interpretable maintenance alerts rather than solely numerical life estimates. This study investigates a condition-aware data-driven framework for jet engine health assessment using the NASA C-MAPSS dataset, which contains four benchmark subsets (FD001–FD004) with different operating conditions and fault modes. Instead of formulating the task as conventional remaining useful life regression, this study reformulates degradation assessment as a three-class health state classification problem, including Normal, Warning, and Fault. A unified preprocessing pipeline is developed, incorporating condition-wise normalization, first-order differential feature construction, and per-unit sliding window segmentation to reduce operating-condition bias, capture degradation dynamics, and prevent data leakage. Five representative models are evaluated under the same framework, including XGBoost, LightGBM, Random Forest, a context-aware multi-scale temporal attention convolutional neural network, and a bidirectional long short-term memory network. The results show that the proposed framework achieves consistently high classification accuracy across all four subsets, with the best results of 0.9841 on FD001, 0.9764 on FD002, 0.9891 on FD003, and 0.9832 on FD004. In addition, Bi-LSTM outperforms MSTA-CNN on all subsets, for example improving accuracy from 0.9614 to 0.9747 on FD002 and from 0.9773 to 0.9806 on FD004, which is consistent with the importance of long-term temporal dependency modeling for this task. These findings suggest that the proposed framework provides an effective and maintenance-decision-aligned solution for C-MAPSS-based health monitoring, where the three-class alert output offers clearer operational meaning than a single numerical life estimate. Full article

Accurate short-term prediction of network service traffic is essential for communication resource allocation and proactive fault warning in smart grids. However, smart grid service traffic is characterized by nonlinear fluctuations, strong spatio-temporal coupling, and considerable uncertainty, making it difficult for existing methods to capture long-range dependencies, adapt to dynamic topological relationships, and reflect prediction risks. To address these issues, this work develops a deep learning framework that integrates a spatio-temporal Transformer with a diffusion mechanism. The spatio-temporal Transformer extracts temporal evolution patterns and spatial logical correlations from historical traffic matrices, while the diffusion module improves robustness to abrupt traffic variations through latent uncertainty modeling. Furthermore, attention-guided recurrent units are used to generate stable multi-step forecasting sequences. Experiments on a real-world network dataset show that, compared with mainstream benchmark models, the proposed framework reduces Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Relative Squared Error (RRSE) by 46.62%, 47.05%, and 44.18%, respectively. These results indicate that the framework improves prediction accuracy and stability while alleviating error accumulation in long-horizon forecasting, thereby providing reliable technical support for smart grid network management. Full article

Lubricating oil monitoring provides continuous health information for early fault warning and maintenance decision-making in industrial gas turbines. However, real-world multi-sensor monitoring streams exhibit pronounced nonstationary thermodynamic drifts that often obscure subtle high-frequency residuals containing critical incipient degradation signatures. Prevailing data-driven monitoring models typically struggle to separate these macroscopic trends from stochastic wear-related fluctuations, and their restrictive distributional assumptions are often inadequate for the heteroscedastic and heavy-tailed nature of industrial residuals. To address these challenges, this study proposes ResAD-Net, a framework for early fault warning in nonstationary multi-sensor oil monitoring that combines trend–residual decoupling, trend-conditioned residual modeling, and residual-domain dependency learning. Specifically, a signal trend–residual decoupling strategy is adopted to separate slowly varying operational trends from stochastic residual fluctuations captured by the sensors, thereby exposing residual information that is more sensitive to incipient degradation. On this basis, a trend-conditioned diffusion model is introduced to characterize state-dependent, skewed residual distributions and generate residual sample ensembles for nonstationary monitoring. Meanwhile, a graph-based variational autoencoder is employed to learn latent intersensor dependency structures from the residual domain, providing diagnostic cues for temporal risk evolution analysis and sensor-level inspection. Experiments on a real-world industrial oil-monitoring record show that the proposed framework achieves an average F1-score of 0.985 with no observed false positives in the predefined pre-alarm reference interval of the finite test set. In addition to accurate anomaly detection, ResAD-Net captures early residual distributional shifts before clear macroscopic deviations emerge and provides diagnostic association cues for interpreting oil-monitoring changes around the system-level alarm. Full article

The initial seconds after an earthquake are critical for rapid magnitude estimation to support real-time early warning. This study evaluates the determination of P-wave moment magnitude (M_wp) using strong-motion records from the 23 April 2025 Marmara Sea earthquake. High-quality accelerometric data from the Turkish National Strong Motion Network were analysed to extract early P-wave features within the first 3 s after P-wave onset. Results show significant rupture-directivity effects, whereby stations located approximately along the fault strike and rupture-propagation direction recorded larger ground-motion amplitudes and higher station-based Mwp estimates than stations located near nodal directions. The mean M_wp was 6.5 ± 0.2, consistent with the Global Centroid Moment Tensor (GCMT) moment magnitude estimate. Magnitude estimation was achievable within 8–20 s of P-wave arrival, confirming the method’s real-time applicability. Our findings demonstrate that strong-motion P-wave analysis can provide rapid and reliable magnitude estimates suitable for earthquake early warning, tsunami warning, and rapid-response applications. In the Marmara Sea region, where tsunami arrival times may be on the order of 20–30 min and critical infrastructure is concentrated in densely populated coastal areas, rapid determination of magnitude within seconds of earthquake initiation can provide valuable information for emergency management and hazard mitigation decisions. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

The accurate location of leakage points is a key part of underground gas prevention. To solve the problem of low positioning accuracy for gas extraction pipeline leakage, the gas extraction pipeline leakage experimental system was built, and the multi-source collaborative leakage localization method based on Stacking learning was proposed. The results showed that the Stacking–LSSVM–Elman–DBN (S-L-E-D) model with pressure–flow collaborative input achieved the best localization performance, with an accuracy of 0.932, Root Mean Square Error (RMSE) of 0.053, Mean Absolute Percentage Error (MAPE) of 0.082, Theil Inequality Coefficient (TIC) of 0.056, and a distance error below 1 m. Compared with a single-parameter input, the collaborative pressure–flow input improved the localization accuracy by more than 10%, while the RMSE and MAPE decreased by 39.0% and 37.4%, respectively. Under monitoring point fault conditions, the localization accuracies of monitoring points 1, 4, and 5 were 0.884, 0.891, and 0.881, respectively, while the dual-fault condition of monitoring points 1 and 4 still maintained an accuracy of 0.861. The study provides a feasible multi-source collaborative learning framework for leakage localization in gas extraction pipelines and offers a methodological reference for improving leakage monitoring and early warning. Full article

With the continuous growth of urban building density and elevator deployment, the reliability, maintenance, and degradation risk warning of elevator systems have attracted increasing attention. Conventional monitoring methods based on fixed thresholds or rule logic are easy to implement, but they often fail to identify progressive degradation and are sensitive to complex operating conditions and measurement noise. This paper proposes a physics-informed neural network (PINN)-based method for elevator health monitoring and early warning. First, multi-sensor data are processed through time alignment and feature reconstruction, and a dual-path acceleration estimation method is introduced to improve the stability of dynamic state calculation. Second, a simplified traction elevator dynamic model considering load variation, motor drive, and mechanical resistance is embedded into PINN training to identify hidden parameters. Electrical and dynamic residual indicators are then constructed to characterise system condition from different physical perspectives. Finally, a time-accumulated risk model combining anomaly magnitude and persistence duration is developed to detect progressive degradation trends. Results show stable parameter convergence and effective condition assessment. The proposed approach detects degradation trends earlier than conventional threshold-based monitoring methods and reduces false alarms caused by transient disturbances. It provides an interpretable and practical solution for predictive maintenance and intelligent operation of elevator systems. Full article

Early fault prediction of wind turbines is critical for ensuring wind farm safety and reducing operation and maintenance costs. However, the latent and progressive nature of incipient faults, together with concurrent failures across multiple subsystems, makes accurate root-cause identification challenging. In addition, severe class imbalance between normal and faulty samples further degrades prediction performance, particularly for minority fault types. To address these challenges, this paper proposes a novel fault prediction model, M²ANet, using SCADA data within a 30-min pre-fault window. The model combines a dual-memory module with progressive dilated convolutions to efficiently capture multi-scale temporal dependencies from high-dimensional operational variables. An entropy-bias penalty is further introduced into the loss function to adaptively regularize the predicted probability distribution, alleviating overconfidence under imbalanced data conditions and improving the recognition of minority faults. Experiments on a real-world wind farm dataset show that M²ANet achieves an overall accuracy of 90.73% and a weighted F1-score of 90.62% in multi-class fault prediction, outperforming 10 representative baseline models. In addition to these aggregate metrics, per-class evaluation confirms the model’s robustness under class imbalance. Notably, for yaw system faults, which account for only 1.9% of the samples, M²ANet achieves a recall of 95.92% with a 30-min-ahead warning. These results demonstrate its effectiveness and reliability for early fault prediction in practical wind turbine applications. Full article

The safety of autonomous vehicles depends not only on perception and planning, but also on the correctness of low-level electronic signals that connect controllers and actuators. Errors at this interface, caused by hardware degradation, timing violations, software faults, or unexpected interactions, can lead to unsafe behavior even when high-level autonomy functions operate correctly. Existing safety mechanisms primarily focus on system behavior, trajectories, or controller design, leaving actuator-bound command streams largely unmonitored. This paper proposes a low-level, AI-enabled anomaly-detection layer for autonomous vehicle architectures. The core idea is to embed a lightweight observer within a virtualized master controller to monitor control-signal streams in real time without interfering with the primary control logic. The proposed framework combines a stacked LSTM sequence classifier with rule-based safety constraints and context-aware monitoring to detect physically implausible or temporally inconsistent command behavior before actuation. A proof-of-concept simulation study was conducted to evaluate the practicality of the approach using overtaking scenarios in a co-simulated high-level and low-level environment. The results show that the proposed concept can identify severe abnormal low-level behavior and provide preliminary warning/error indications, supporting its potential as a complementary safety layer at the control-to-actuation interface. Full article

In-wheel motor bearings in electric vehicles operate in harsh environments where strong background noise often masks early fault features, limiting the accuracy of traditional diagnostic methods. This study proposes an intelligent fault diagnosis framework integrating improved Successive Variational Mode Decomposition (SVMD) with a ResNet–Kolmogorov–Arnold Network (ResNet-KAN). To enhance feature extraction, a multi-strategy Crested Porcupine Optimizer (CPO) is employed to adaptively optimise SVMD parameters. Subsequently, a Gramian angular difference field (GADF) reconstruction strategy transforms one-dimensional vibration signals into two-dimensional images to improve spatial distinguishability. Finally, a ResNet-KAN model, featuring a ReLU-based non-linear classification head, is developed to capture complex fault boundaries more effectively than traditional linear layers. Experimental results demonstrate that the CPO-SVMD method increases the kurtosis of extracted components by at least 25.6% compared to traditional optimisation methods. Furthermore, the ResNet-KAN model achieves an identification accuracy exceeding 98% on the in-wheel motor bearing dataset, outperforming 2DCNN, ResNet, and ViT models by at least 2%. This integrated approach provides a robust, high-precision solution for the intelligent condition monitoring and early warning of in-wheel motor drive systems under complex, high-noise operating conditions. Full article