Search Results (566)

In response to the frequent safety incidents associated with the core electrical systems (i.e., traction battery, charging system, and drive motor) of new energy vehicles (NEVs) and the lack of forward-looking quantitative risk assessment methods in existing detection and diagnostic technologies, this study introduces the Layer of Protection Analysis (LOPA) methodology into the field of NEV safety. Unlike qualitative methods (e.g., FMEA, FTA) or purely data-driven diagnosis, this work establishes a tailored semi-quantitative LOPA framework that defines scenario-specific independent protection layer (IPL) identification criteria and probability of failure on demand (PFD) assignment rules for NEV applications. Typical risk scenarios, including battery thermal runaway, electrical faults in charging systems, overheating of drive motors, and battery internal short circuits caused by mechanical abuse, are systematically analyzed in terms of their failure mechanisms and evolution processes. A tailored quantitative risk assessment framework is established and applied to conduct full-process risk evaluations for the four scenarios. The results indicate that, under the synergistic effect of multiple protection layers—including inherently safe design, basic process control systems, safety instrumented systems, and physical protection measures—the accident consequence frequencies of all scenarios are significantly lower than the tolerable risk thresholds. This verifies the applicability and effectiveness of the LOPA method in NEV safety analysis. The proposed quantitative framework provides a scientific basis for safety design optimization, identification of critical protective elements, and operation and maintenance strategy formulation throughout the lifecycle of NEVs. Furthermore, the limitations of data portability from process industries are discussed, and sensitivity analyses are conducted to confirm the robustness of the conclusions. Full article

This paper proposes a spectrum-aware generative learning framework for intelligent motor fault diagnosis under small-sample conditions. To address the challenges of insufficient labeled fault data and imbalanced distributions in motor systems, a hybrid model integrating a generative adversarial network (GAN) with an attention-enhanced deep neural network is developed. First, vibration signals of the motor are transformed into time–frequency representations to capture discriminative spectral features. Then, the GAN is employed to augment minority classes and improve data diversity, while the SE (squeeze-and-excitation) mechanism enhances feature extraction by emphasizing critical fault-related components. Finally, a deep classifier is trained on the augmented dataset for fault identification. Experimental results on benchmark datasets demonstrate that the proposed method achieves superior diagnostic accuracy and robustness compared with several state-of-the-art approaches, especially under severe data scarcity and imbalance scenarios. The results indicate that the proposed framework effectively improves generalization performance and provides a reliable solution for intelligent motor fault diagnosis in practical industrial applications. Full article

Induction motors are critical in modern industry, powering over 70% of industrial processes. Reliable operation is essential to minimize downtime and ensure production continuity. This paper proposes an integrated multimodal methodology for fault diagnosis and prognosis in induction motors, based on an extended Pearson and Gain feature fusion framework. The approach preprocesses vibration, current, voltage, torque, and speed signals through denoising, normalization, synchronization, and sliding-window segmentation. Over 200 features per window are extracted across time, frequency, envelope, wavelet, harmonic, slip-based, and MCSA domains. A key innovation is correlation-driven multimodal fusion, combining Pearson correlation, spectral coherence, cross-spectral energy, and mutual information to produce Gain-enhanced features with improved discriminative capability. Fault diagnosis is performed using RF, SVM, XGBoost, and MLP models, with time-aware data splitting to avoid temporal leakage. Prognosis employs a continuous Degradation Index (DI) modeled via Gaussian Process Regression for uncertainty-aware prediction, with failure probability and Remaining Useful Life (RUL) estimated from DI thresholds. Experimental results demonstrate that the proposed methodology achieves diagnostic accuracy above 97%, enhances feature relevance, and provides stable long-term prognostic performance, offering a robust framework for predictive maintenance of induction motors. Full article

Accurate demagnetization fault diagnosis is critical to ensuring the safety and reliability of permanent magnet synchronous motors (PMSMs). However, the number, location, and severity of demagnetized permanent magnets are mutually coupled, leading to a combinatorial explosion of fault patterns. Existing methods are largely limited to idealized assumptions involving single-magnet demagnetization or uniform demagnetization of multiple magnets, making it difficult to characterize the random nature of demagnetization in practical operation. Thus, this paper proposes a precise demagnetization fault diagnosis method based on a novel search coil (SC) configuration, in which only two toroidal-yoke-type search coils are installed in the stator slots. The proposed method partitions the rotor permanent magnets into several modules and categorizes the infinite demagnetization fault patterns into 26 representative patterns, effectively addressing the issue of fault mode explosion. Theoretical analysis and experimental results show that the voltage waveforms of the search coil over a single electrical period exhibit significant and stable differences across the identified patterns. By constructing feature vectors based on these differences, a physically interpretable mapping between the feature vectors and fault patterns is established. Combined with a corresponding pattern recognition algorithm, the proposed method enables fast and accurate differentiation of the 26 patterns without the need for complex machine learning models, thereby achieving precise localization of demagnetized permanent magnets. Simulation and experimental results verify the correctness and effectiveness of the proposed method. Full article

Substantial progress has been made in bearing-fault classification under same-condition settings, yet cross-condition diagnosis remains affected by three coupled issues: speed-induced temporal-scale perturbation, insufficient use of structured label information, and domain shift across operating conditions. To address these issues, this paper presents BearingPRO, a unified framework that combines physics-guided representation alignment with group ordinal labeling for bearing-fault classification. The framework contains three modules. First, a TimeWarp module applies controlled temporal stretching and compression to emulate waveform variations induced by speed changes. Second, a Grouped Ordinal module introduces intra-group ordinal constraints according to the hierarchical relation between fault type and fault severity. Third, a Physics-Guided Representation Alignment (PGRA) module uses rotational-speed priors for carrier-frequency calibration, envelope extraction, and cross-domain alignment. On the CWRU bearing dataset, under the 0 HP → 3 HP transfer task, BearingPRO achieves 0.9205 ± 0.0088 accuracy and 0.8962 ± 0.0105 Macro-F1 in the unified reproduction setting used in this study. Relative to the re-implemented comparison methods under the same backbone and training budget, the proposed framework yields higher mean performance and lower variance. Ablation results further indicate that temporal-scale modeling, grouped ordinal supervision, and physics-guided alignment play complementary roles in the current setting. At the same time, the scope of the conclusion is explicitly bounded: the present evidence is obtained on the CWRU test rig, within the considered speed range, and under artificially introduced point defects; therefore, the method is presented as a well-supported cross-condition classifier for this benchmark, not as a universally validated solution for all motors. Full article

Intelligent fault diagnosis of rotating machinery is essential for manufacturing reliability and predictive maintenance, yet deployment of deep learning models is limited by data scarcity: fault samples are rare, costly, and hazardous to obtain. Conventional synthetic data methods such as Generative Adversarial Networks and Variational Autoencoders often exhibit mode collapse, spectral distortion, and limited physical interpretability. This work presents MechaForge, a multi-strategy framework that employs Large Language Models (LLMs) as physics-guided generators for bearing fault time-series data. The approach is grounded in bearing kinematics, Motor Current Signature Analysis (MCSA), and the interpretation of in-context learning as implicit Bayesian inference. Within MechaForge, four progressively constrained tracks are defined: a real-data baseline, few-shot LLM mimicry, multi-stage semantic reasoning, and physics-guided generation with constraints on root mean square, kurtosis, and fault-band spectral energy. For direct benchmarking, conventional VAE- and GAN-based augmentation baselines are additionally evaluated under the same dataset split, synthetic-data budget, downstream CNN architecture, and evaluation metrics. Experiments on the Paderborn bearing dataset show that the Basic LLM track achieves the strongest performance under the present protocol (0.7862 accuracy, 0.7648 macro-F1), exceeding the added VAE and GAN baselines (both 0.7428 accuracy; 0.7202 and 0.7257 macro-F1, respectively), while a control experiment confirms that synthetic data provides discriminative structure rather than labeled noise. These results indicate the promise of LLM-based diagnostic augmentation under data scarcity in the present Paderborn setting, rather than a definitive demonstration of broad transferability across fault-diagnosis scenarios. Full article

As a critical element of the drive motor, rolling bearings are susceptible to localized defects under complex loads and varying operating conditions. Such defects typically generate periodic transient shocks, which reflect bearing fault features. However, the accurate extraction of fault-related transient components becomes challenging due to strong noise influence. To address this issue, a concave sparsity-assisted generalized dispersive mode decomposition (CSA-GDMD) method is developed to enhance fault feature extraction. This method introduces a non-convex sparse model based on generalized mini-max concave (GMC) regularization to preprocess the vibration signal. The GMC penalty effectively suppresses background noise while better preserving the amplitude characteristics of the transient shocks. Subsequently, GDMD is applied to progressively extract transient shock components from the preprocessed signal and reconstruct the signal, resulting in more prominent fault-related transient components. The simulation results show that CSA-GDMD significantly improves the signal-to-noise ratio (SNR), from 6.5905 dB at −15 dB to 9.5122 dB at 5 dB, and reduces the root mean square error (RMSE) from 0.0280 to 0.0196. Consequently, the fault feature frequencies can be identified more clearly in the envelope spectrum, further confirming the accurate fault diagnosis capability of the proposed method for bearing faults under strong noise conditions. Full article

Reliable fault detection in brushless DC motors is challenging owing to environmental complexity and high equipment costs. To address these challenges, we propose an effective and cost-effective approach using an optimized end-to-end one-dimensional convolutional neural network. Specifically, a real experimental platform simulating bearing and eccentricity faults was developed. Statistical t-tests indicated that three-axis accelerometer signals from a low-cost inertial measurement unit provided sufficient fault information for the present diagnosis task. Unlike traditional methods such as support vector machines, multilayer neural networks, and random forests, which rely on manual feature extraction, our model learns directly from raw waveforms and can handle signal drift. Under the present controlled experimental setting and the leave-one-day-out evaluation protocol, the model achieved 100.00% average window-level classification accuracy, considerably outperforming traditional methods, the performances of which declined to 67.95–71.37% under environmental shifts. Moreover, with an inference time of only 0.96 ms, 32 times faster than that of random forests, this approach is well suited for real-time embedded monitoring. The proposed method demonstrates strong potential for cost-efficient and robust fault diagnosis under the present experimental setting. Full article

Traditional approaches for identifying bearing defects in metro traction systems often suffer from low diagnostic efficiency and accuracy. To address this, we propose an information fusion approach using the Bat Algorithm-Optimized Variational Mode Decomposition (BA-VMD) and the Self-Attention Convolutional Neural Network (SA-CNN). Vibration and acoustic emission signals are denoised via BA-VMD to optimize decomposition, followed by a diagnosis model utilizing attention-based fusion and SA-CNN to enhance key feature extraction. Experiments on subway traction motor bearings under varying operating conditions demonstrate the method’s efficacy. Results indicate that BA-VMD achieves a signal-to-noise ratio of 6.791, which is 1.595 higher than that of EMD (5.196). Furthermore, the SA-CNN model achieves an average diagnostic accuracy of 98.6%, significantly outperforming MLP (93.57%) and SVM (90.90%). These findings confirm that the proposed framework ensures accurate and stable bearing fault detection in highly variable operating conditions. Full article

Maintaining safe and consistent performance in industrial energy networks necessitates the dependable detection of asynchronous motor failures. However, in practical scenarios, diagnostic models often suffer from poor generalization and high false alarm rates when faced with incomplete datasets and limited high-quality samples. Aiming to overcome the aforementioned constraints, a PCA-KPLS integrated multi-fidelity scheme is presented in this work. The method utilizes low-fidelity data to construct a Principal Component Analysis (PCA) model for extracting basic features, and then integrates a small amount of high-fidelity target data via Kernel Partial Least Squares (KPLS) to establish a cross-domain feature mapping, enabling knowledge transfer between data of different fidelities. Validation through mathematical simulation and an engineering case study on a primary air fan demonstrates that the proposed method achieves higher prediction accuracy and lower root-mean-square error compared to models using only low-fidelity or high-fidelity data, significantly reduces false alarms, and enhances the accuracy of fault diagnosis and model generalization capability when training samples are insufficient. Full article

Motor fault diagnosis under small-sample conditions remains challenging because limited labeled data often cause deep models to overfit and generalize poorly. To address this problem, we propose STR-DDPM, a fault data augmentation framework that combines moving-average-based seasonal–trend–residual decomposition with a denoising diffusion probabilistic model. Specifically, multichannel signals are decomposed into trend, seasonal, and residual components, and class-conditional diffusion modeling is performed only in the residual domain. This design emphasizes fault-related stochastic variations while reducing interference from deterministic structures. To improve generation stability, we adopt velocity prediction and develop an enhanced one-dimensional U-Net with multi-scale convolutions, channel attention, self-attention, and feature-wise linear modulation for controllable conditional generation. Experiments on the University of Ottawa and Paderborn motor fault datasets demonstrate that the proposed method generates samples that are highly consistent with real data and improves diagnostic performance under multiple synthetic-data-assisted settings. These results indicate that STR-DDPM provides an effective and practical solution for data augmentation in data-limited motor fault diagnosis. Full article

Acoustic signal-based fault diagnosis offers a promising non-contact approach for rotating machinery. However, its practical application is usually affected by environmental noise. This paper presented a Denoising Attention Domain Adversarial Network (DDAN) for the robust fault diagnosis of wheel hub motors under severe noise interference. The proposed framework consists of the following two core modules: a DenseNet-based denoising module that adaptively suppresses background noise while retaining critical fault features, and a Stacked Autoencoder Domain Adversarial Network (SADAN) that integrates channel attention, spatial attention, and multi-head self-attention (MHSA) for refined feature extraction and classification. Such a hierarchical attention mechanism facilitates effective local noise suppression and global dependency capture. Validation on a hub motor fault dataset and publicly available online dataset demonstrates that compared to existing methods, DDAN achieves superior diagnostic accuracy across various noise levels and signal-to-noise ratios, improving SNR from -15.97 dB to 1.24 dB, achieving 82.71% accuracy under low SNR condition, and reaching 84.93% and 83.75% accuracy in cross-domain generalization tests. Furthermore, the comparison of the diagnostic accuracy of audio signals from different acoustic acquisition devices further verifies the practicality and potential of the system in low-cost industrial deployment. Full article

Permanent magnet synchronous motors (PMSMs) have been widely used in new-energy vehicles and industrial servo systems. However, demagnetization faults (DMFs) can lead to severe issues, including torque ripple and magnetic field distortion. This paper proposes an intelligent diagnostic approach for DMFs based on stator tooth flux (STF). A mathematical model of STF is formulated, and the magnetic flux change is measured using multiple sets of anti-series-connected detection coils (DCs). By combining finite element simulation with signal processing technology, we establish a comprehensive diagnostic system covering fault feature extraction, fault location identification, and severity assessment is established. The proposed method employs wavelet transform (WT) to extract time-frequency features of voltage signals and combines it with a convolutional neural network (CNN) to form the WT-CNN intelligent diagnosis model. Based on the extracted voltage signal features, the method achieves intelligent identification and visual localization of DMFs. Simulation results show that the proposed method achieves an accuracy above 80% for fault location identification (defined as sample-level multi-label classification accuracy across 12 PMs) and above 85% for demagnetization severity estimation (defined as classification accuracy across 9 severity degrees from 10% to 90%). These results provide an effective technical foundation for motor condition monitoring and fault early warning in simulation environments. Full article

In this study, we propose a data-driven diagnostic system that uses Extreme Gradient Boosting (XGBoost) to detect, classify, and assess the severity of multiple faults in permanent magnet synchronous motors (PMSMs). The three main fault categories that are the focus of the suggested method are inter-turn short-circuit (ITSC) faults, stator open-circuit faults, and permanent magnet demagnetization. To capture fault-specific characteristics and their development with severity, discriminative electrical features are retrieved from stator currents, flux linkage, and dq-axis values. Next, using the chosen electrical indications, an aggregated diagnostic index is created to facilitate defect diagnosis and severity quantification in a single learning process. The XGBoost-based model has been shown to produce excellent diagnostic accuracy and robust separation between various fault causes via extensive assessment. It also maintains dependable performance under previously unknown operating or fault situations. These findings show that an XGBoost-only approach offers a scalable and efficient way to monitor advanced PMSM conditions in industrial and safety-critical applications. Full article

Bearings play crucial roles in industrial machinery. Therefore, the continuous monitoring and effective detection of bearing failures are essential to ensure the safety and reliability of motors. Traditional fault diagnosis methods often require information from both the time and frequency domains; however, converting them into a two-dimensional representation significantly increases computational costs. Conversely, utilizing only time-domain features while ignoring frequency-domain features results in incomplete fault information, reducing accuracy under various operating conditions. This study proposes an efficient dual-stream network with soft-gated fusion for bearing fault diagnosis that simultaneously analyzes acoustic emission signals in the time and frequency domains. Our approach employs two separate feature-learning branches: the time-domain branch directly extracts features from the segmented raw acoustic emission signals, and the frequency-domain branch learns features from one-dimensional spectral vectors obtained using the fast Fourier transform. A gated fusion mechanism adaptively balances the contribution of each domain before classifying fault types. The experimental results show that the proposed method significantly reduces the computational cost compared with that of a two-dimensional-representation-based model and improves accuracy over time-only or frequency-only baselines. Full article