Search Results (104)

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Aiming at the problems that existing detection methods struggle to accurately identify early insulation faults of induction motors, are susceptible to interference, and have poor installation adaptability, a non-intrusive detection method for early insulation faults of induction motors based on a microstrip antenna array is proposed. Relying on the low-loss electromagnetic wave transmission characteristic of the heat dissipation hole at the tail of the induction motor, a four-element microstrip antenna array with multiple narrow beams and dual detection frequencies is designed, with the detection frequencies accurately set at 1.14 GHz and 2.23 GHz, which effectively avoids the motor operation noise frequency band (≤300 MHz) and the strong interference frequency band of mobile base stations (900 MHz, 1.8 GHz, 2.4 GHz). Utilizing the high gain and strong directivity of the array antenna, the accurate extraction and amplification of weak electromagnetic wave signals from early insulation fault discharge penetrating through the heat dissipation hole are realized. The full-dimensional simulation design of the antenna array is completed by using HFSS electromagnetic simulation software, and an industrial-grade experimental platform is built to carry out multi-condition verification experiments. The results show that the proposed detection system can realize non-intrusive, non-stop, and non-disassembly identification of early insulation discharge faults in induction motors, with a fault recognition rate of 94% for single faults and 90% for composite faults, and the average signal-to-noise ratio reaches 31.6–35.2 dB. Even under strong industrial electromagnetic interference, the recognition rate remains above 85%. This method overcomes the problems of traditional methods such as severe noise interference, difficult installation, and inability to monitor online, providing a high-efficiency scheme for real-time insulation state monitoring of industrial induction motors with good engineering application value. Full article

Incipient faults in power cables are difficult to diagnose because their transient signatures are weak, non-stationary, and easily masked by background noise, while labeled real-world samples are often scarce. To address these challenges, this paper proposes an offline diagnosis framework that integrates Whale Optimization Algorithm (WOA)-guided CEEMDAN with a TCN-BiLSTM-Multi-HeadAttention network. The proposed method has three main features. First, WOA is explicitly mapped to the CEEMDAN parameter optimization problem and is used to adaptively optimize the noise amplitude and ensemble number, thereby improving decomposition quality and enhancing weak fault-related components. Second, the optimized intrinsic mode functions are reconstructed into a multi-channel representation that preserves complementary fault information across different frequency bands. Third, a hybrid deep architecture combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory, and multi-HeadAttention is designed to jointly capture local transient characteristics, bidirectional temporal dependencies, and fault-sensitive feature interactions. Experimental results on both PSCAD/EMTDC simulation data and real-world measured data show that the optimized WOA-CEEMDAN achieves superior decomposition performance, with an RMSE of 0.097 and an SNR of 8.42 dB. On the real-world test dataset, the proposed framework achieves 96.00% accuracy, 97.25% precision, 96.84% recall, an F1-score of 0.970, and an AUC of 0.97, outperforming several representative baseline models. Additional ablation, noise-robustness, small-sample, confusion-matrix, and cross-cable validation results further demonstrate the effectiveness and robustness of the proposed framework for incipient cable fault diagnosis. Full article

Rolling bearing fault diagnosis under complex and noisy operating conditions requires not only high diagnostic accuracy but also interpretability that can be quantitatively verified against physically meaningful excitation structures. However, many existing deep learning approaches rely on a single time–frequency (TF) representation and provide limited, non-verifiable links between model decisions and the original vibration patterns. To address this issue, we propose MBT-XAI, a multi-wavelet TF fusion network with a Token-to-Spectrum Traceback (TST) mechanism for structure-preserving, physics-consistent interpretability. Three complementary wavelets, namely Morlet, Mexican Hat, and Complex Morlet, are used to construct multi-view TF representations, which are encoded into RGB channels and adaptively fused via cross-channel attention within a Transformer backbone. TST maps patch-token attributions back to the TF domain, enabling quantitative evaluation of physics consistency through overlap-based metrics. Experiments on the public CWRU dataset and an industrial IMUST dataset show that MBT-XAI achieves 98.13 ± 0.24% and 96.23 ± 0.31% accuracy at SNR = 0 dB, outperforming the strongest baseline by 2.83% and 2.43%, respectively. Under AWGN contamination, MBT-XAI maintains 95.44 ± 0.38%/93.45 ± 0.47% accuracy on CWRU and 95.80 ± 0.33%/92.91 ± 0.51% accuracy on IMUST at SNR = −2/−4 dB. Under colored-noise contamination, the proposed method also preserves robust performance under pink and brown noise at the same SNR levels. Quantitative interpretability evaluation further indicates high alignment between salient frequency regions and theoretical fault-characteristic bands, with IoU = 80.21 ± 0.86% and Coverage = 91.70 ± 0.63%. In addition, MBT-XAI requires 10.393 M parameters and 10.678 GFLOPs, with an inference latency of 14.7 ms per sample (batch size = 1) on an NVIDIA GeForce RTX 3060 GPU. These results suggest that multi-wavelet TF modeling with attention-based fusion and TF-level traceback provides an accurate, robust, and physics-consistent framework for intelligent bearing fault diagnosis. Full article

The detection of sulfur hexafluoride (SF₆) decomposition products is critical for diagnosing insulation faults in gas-insulated switchgear (GIS). In this study, a vanadium-doping strategy was incorporated into the graphene/MoS₂ (GM) heterojunction to design a vanadium-doped graphene/MoS₂ (GMV) heterojunction material. Leveraging first-principles density functional theory (DFT), the adsorption behaviors of five characteristic SF₆ and its decomposition gases (H₂S, SO₂, SOF₂, SO₂F₂) on intrinsic GM and GMV were systematically investigated to evaluate their potential for gas sensing applications. Computational results reveal that intrinsic GM exhibits only weak physical adsorption toward all target molecules, with low adsorption energies and negligible charge transfer, which fails to meet practical application requirements. In contrast, GMV demonstrates significantly enhanced adsorption energies for H₂S, SO₂, and SOF₂ at vanadium sites (with a maximum value of −0.388 eV for SO₂) and shorter adsorption distances, while SO₂F₂ and SF₆ preferentially adsorb near electron-deficient carbon regions. Intrinsic GMV displays semimetallic properties, with a Fermi level at 0.126 eV and a band gap of 0.0017 eV. Upon adsorption of H₂S, SOF₂, SO₂F₂, or SF₆, the Fermi level undergoes a moderate shift (ranging from −1.083 eV to +0.349 eV), with minimal changes in the band gap. Conversely, SO₂ adsorption induces a substantial downward shift of the Fermi level to −1.732 eV, accompanied by the emergence of a sharp partial density of states (PDOS) peak near the Fermi level (0–1.5 eV), indicating strong orbital coupling and significant charge transfer. Furthermore, recovery times calculated using classical formulas show that at room temperature and a frequency of 1 × 10⁶ Hz, the recovery time of GMV for SO₂ is 2.43 s, outperforming the other four gases and satisfying practical gas sensing requirements. Through comprehensive analysis of adsorption distances, electronic structure changes, and recovery times, GMV exhibits higher selectivity toward SO₂. Thus, GMV can serve as a sensing material for detecting GIS insulation faults associated with elevated SO₂ concentrations, offering a viable strategy for advancing online monitoring technologies in power systems. Full article

Early fault vibration signals from rolling bearings are typically nonlinear, non-stationary, and heavily obscured by background noise, which severely impedes the accurate extraction of fault features. To overcome the limitations of traditional stochastic resonance (SR)—specifically the small-parameter restriction for high-frequency signals and the subjectivity in parameter selection—this paper proposes an adaptive SR envelope spectroscopy method based on particle swarm optimization (PSO) and local mean decomposition (LMD). First, a variable-scale transformation is introduced to compress the high-frequency fault signals into the effective frequency band required by the adiabatic approximation theory. Second, utilizing the global search capability of PSO, the potential well parameters of the bistable system are adaptively optimized by maximizing the output signal-to-noise ratio (SNR), thereby achieving optimal matching between the nonlinear system and the input signal. Finally, the enhanced signal is decomposed by LMD, and the sensitive components are selected for envelope spectrum analysis to identify fault characteristics. Experimental validation using the Case Western Reserve University bearing dataset demonstrates that the proposed method effectively amplifies weak fault signals under strong noise conditions, exhibiting superior feature extraction accuracy and noise robustness compared to traditional methods. Full article

Incipient fault signatures in rolling bearings are often compromised by intense background noise and stochastic impulses. Conventional resonance demodulation frequently relies on rigid frequency partitioning, which tends to disrupt the physical continuity of resonance bands and results in the incomplete capture of essential diagnostic information. Furthermore, the robustness of prevailing optimal demodulation frequency band (ODFB) selection indicators remains limited under heavy noise interference. This study develops the WLERgram framework, which utilizes regularized Fourier series to capture the global morphology of the vibration spectrum. By anchoring filter boundaries at natural energy troughs, the method mitigates spectral truncation based on inherent signal characteristics. The framework integrates an Adaptive Morphological Consensus (AMC) strategy, employing multi-scale operators to extract rotation-correlated components and enhance resistance to incoherent interference. By incorporating a Weighted Logarithmic Energy Ratio (WLER) metric, the method utilizes a nonlinear operator to implement differential mapping between coherent fault harmonics and stochastic noise, enabling autonomous optimization of the demodulation band. Validations using synthetic simulations and experimental benchmarks (CWRU and UORED) suggest that WLERgram offers reliable feature extraction performance and diagnostic robustness under harsh noise environments. Full article

Aiming at the problem that insulation fault location in multi-branch cable networks of train DC medium-voltage power supply systems is disturbed by impedance mismatch at the injection end, this paper proposes an impedance matching method based on a resistance–capacitance composite coupling network to suppress false reflections and improve location accuracy. Firstly, the mechanism of multiple false reflections caused by injection-end mismatch is theoretically analyzed, and the expression of reflected waves and their influence on waveform superposition are derived. On this basis, an RC coupling network is designed to achieve effective matching between the measurement end and the cable characteristic impedance (about 97 Ω) within the 8–12 MHz frequency band, suppressing the amplitude of false reflections to less than 2% of the incident wave. Verification through MATLAB R2022b/ANSYS Q3D 2024R2 co-simulation and a 1:8 scaled experimental platform shows that the proposed coupling network reduces the absolute fault location error in a multi-branch network from 6.936 m to 0.188 m, decreases waveform distortion by about 40.8% and lowers the equivalent noise enhancement factor by about 55.2%. This study provides a reliable front-end matching solution for accurate fault location in complex cable networks, with clear value for engineering applications. Full article

This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed feature sets. A dataset acquired under variable load and multiple operating conditions was used for model training. Parallel signal processing techniques were applied to capture fault-related information across multiple frequency bands including time-domain analysis, frequency-domain analysis, baseband analysis, and envelope analysis. From the corresponding signal representations, statistical, spectral, and physics-based features associated with characteristic fault frequencies were extracted and combined into integrated feature sets. The diagnostic performance of models trained using purely data-driven features was systematically compared with models incorporating integrated data-driven and physics-informed features. Support Vector Machine, Random Forests, Gradient Boosting, and an ensemble classifier were evaluated using accuracy, precision, recall, and F1-score metrics. The proposed framework employs a two-layer classification strategy, where the first layer performs multiclass fault identification, while the second layer evaluates the presence of imbalance as a coexisting fault. In addition, the influence of different feature groups as well as individual measurement axes and their combinations on diagnostic performance were analyzed. Validation using a new dataset measured in laboratory conditions confirmed the robustness and generalization capability of the proposed diagnostic framework. Full article

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

To address the periodic impacts and amplitude-modulated high-frequency resonance phenomena caused by bearing faults in rotating machinery, this paper proposes a detection method. The core innovation lies in: firstly, constructing a composite fault feature index (CFFI) that integrates normalized kurtosis and fuzzy entropy, which synchronously quantifies the fault impact intensity and periodic structure, and serves as an optimization objective; secondly, definining a spectral energy retention rate (SERR) that includes both the full spectrum and characteristic frequency bands to evaluate the denoising effect and fault feature retention, respectively. Based on this, the method adaptively determines the Variational Mode Decomposition (VMD) parameters through the Triangular Topology Aggregation Optimizer (TTAO), and uses Dynamic Weighted Center Clustering (DWCC) to screen key IMFs containing fault-envelope information. On the IMS bearing dataset, the SERR of the reconstructed signal is 0.21356, which is higher than the actual collected signal value of 0.22465, with a relative error of 4.9%, indicating a higher reconstruction accuracy. These quantitative results indicate that CFFI-guided optimization enhances impulsive and periodic fault components while maintaining stable feature-band retention. This approach is suitable for real-world equipment monitoring and exhibits strong engineering applicability. Full article

To address the strong nonstationarity and complex multi-source interference in vibration signals of bogie axle-box bearings, a fault diagnosis method combining Improved Empirical Wavelet Transform (IEWT) and a Convolutional Neural Network (CNN)–Transformer model is proposed. First, the vibration signals are decomposed using the IEWT method, where dynamic frequency-band division adaptively determines the decomposition bands. This yields multiple intrinsic mode functions, and key modes containing fault features are selected based on information entropy. Next, the selected key modes are fused and transformed into polar coordinate projection maps, further enhancing the distinctiveness of fault data features. Finally, CNN is employed to extract local features from the vibration signals, while the Transformer captures long-range dependencies through the self-attention mechanism, significantly improving feature modeling for complex signals. To validate the fault diagnosis performance of the IEWT and CNN–Transformer model, vibration signals from bogie axle-box bearings in urban railways are analyzed. Analysis of the experimental data suggests that the adaptive decomposition of bearing signals using IEWT effectively overcomes the fixed band boundary limitations of traditional EWT, enhancing the precision of signal feature extraction. The integration of polar coordinate projection maps more accurately illustrates frequency variations and amplitude differences in the signals, fully capturing their nonstationary characteristics. Among the five fault categories of bogie axle-box bearings, the proposed method achieves an accuracy of 99.46%, a recall rate of 99.52%, and an F1-score of 0.995, significantly outperforming five classic comparison methods. This demonstrates that the combined strengths of CNN and Transformer yield higher classification accuracy and better robustness in handling complex fault patterns, effectively solving the fault diagnosis challenges for bogie axle-box bearings. Full article

Arc fault is the dominant cause of fire in photovoltaic (PV) systems, making its accurate identification crucial for PV fire prevention. This study investigates the influence of voltage (200, 300, and 400 V) and current (3, 5, 7, 9, and 11 A) on the DC series arc fault characteristics in PV systems obtained through experimental analysis. The results show that voltage variation has a negligible impact on arc fault behavior, while higher current levels substantially increase noise in the arc fault signals. To effectively mitigate noise, this paper proposes a denoising method that combines an improved moss growth optimization algorithm (IMGO) with improved complete ensemble empirical mode decomposition featuring adaptive noise (ICEEMDAN). It is found that the IMGO-ICEEMDAN denoising algorithm can effectively diminish noise in current signals, broaden characteristic frequency bands, and ameliorate arc feature discernibility. Subsequently, an integrated multi-scale spatiotemporal model is developed to extract arc fault features from the denoised signals. The model employs wide deep convolutional neural networks (WDCNNs) and bidirectional long short-term memory (BiLSTM) networks for parallel feature extraction, supplemented by a cross-attention (CA) module to optimize feature integration. The proposed WDCNN-BiLSTM-CA model ultimately achieves a detection accuracy of 99.89%, demonstrating superior detection performance over conventional methods, such as CNN-GRU and 1DCNN-LSTM models. This work provides a reliable framework for arc fault detection and fire risk reduction in PV systems. Full article

Bearing fault diagnosis is critical in rotating machinery, and collecting and analyzing vibration signals from faulty bearings is a widely employed method in fault diagnosis. To efficiently extract the information of periodic pulse from complex signals and accurately identify fault characteristic frequencies, this paper proposes a BSNA (Bispectral Slice Negentropy Analysis) method. This method leverages the nonlinear characteristics of bispectral analysis and the sensitivity of negentropy measures to transform one-dimensional signals into two-dimensional spectra. By utilizing the demodulation capability of the time-frequency modulation bispectrum, it highlights the relationship between resonance bands and modulation frequency, while maximizing the preservation of critical fault information and minimizing the impact of interference signals. The fault information contained in the slices is subsequently quantified using the CSNE (correlation spectral negentropy), which effectively captures the magnitude of periodic pulse energy. By calculating the CSNE of each modulation frequency slice and visualizing it, the energy distribution of periodic pulses within each slice can be effectively observed. The feasibility of this method in rolling bearing fault diagnosis has been validated through simulation analysis and experimental comparison. This approach enables the accurate identification of fault characteristic frequency and its harmonics, thereby significantly enhancing the accuracy and robustness of fault diagnosis, particularly in complex and noisy background environments. Full article

Non-stationary, multi-component vibration signals in rotating machinery are easily contaminated by strong background noise, which masks weak fault features and degrades diagnostic reliability. This paper proposes a joint denoising method that combines an improved cordyceps fungus optimization algorithm (ICFO), successive variational mode decomposition (SVMD), and an improved wavelet thresholding scheme. ICFO, enhanced by Chebyshev chaotic initialization, a longitudinal–transverse crossover fusion mutation operator, and a thinking innovation strategy, is used to adaptively optimize the SVMD penalty factor and number of modes. The optimized SVMD decomposes the noisy signal into intrinsic mode functions, which are classified into effective and noise-dominated components via the Pearson correlation coefficient. An improved wavelet threshold function, whose threshold is modulated by the sub-band signal-to-noise ratio, is then applied to the effective components, and the denoised signal is reconstructed. Simulation experiments on nonlinear, non-stationary signals with different noise levels (SNR = 1–20 dB) show that the proposed method consistently achieves the highest SNR and lowest RMSE compared to VMD, SVMD, VMD–WTD, CFO–SVMD, and WTD. Tests on CWRU bearing data and gearbox vibration signals with added −2 dB Gaussian white noise further confirm that the method yields the lowest residual variance ratio and highest signal energy ratio while preserving key fault characteristic frequencies. Full article