Search Results (253)

The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault features and high-frequency transient components may reduce the reliability of traditional feature-based diagnosis methods. To improve the representation and classification capability of fault signals, this paper proposes a fault type identification method based on wavelet packet transform and an improved CNN-BiGRU model with a channel attention mechanism. First, three-phase voltage, three-phase current, and zero-sequence voltage signals are decomposed by wavelet packet transform, and the corresponding time–frequency matrices are constructed. Then, these matrices are integrated and converted into time-frequency images, so that multi-source fault information can be represented in a unified form. On this basis, CNN is used to extract local spatial features from the time-frequency images, while BiGRU is employed to capture bidirectional dependency information of fault features. Furthermore, a channel attention mechanism is introduced to enhance informative feature channels and suppress redundant information, thereby improving the fault classification performance. Simulation results based on a 10 kV DG-integrated distribution network show that the proposed method achieves high recognition accuracy under different DG capacities and access configurations. Compared with CNN, BiGRU, and CNN-BiGRU models, the proposed CNN-BiGRU-Attention model shows better classification accuracy and adaptability, demonstrating its effectiveness for fault type identification in active distribution networks. Full article

Reliable fault diagnosis of rotating machinery is critical for averting serious failures in modern industrial systems. While data-driven deep learning has advanced condition monitoring, its success is fundamentally predicated on the availability of independent and identically distributed (I.I.D.) datasets. In realistic operational environments, machinery frequently experiences massive domain shifts induced by varying rotational speeds. Concurrently, acquiring high-fidelity fault instances is limited compared to abundant healthy baseline data, often resulting in a long-tailed distribution. Under such data-starved conditions, conventional few-shot domain adaptation (FSDA) methodologies often may be affected by distributional erasure; global alignment objectives are mainly driven by the healthy majority, causing sparse fault signatures to be erroneously absorbed as noise and leading to severe diagnostic performance degradation. To address this setting, this study develops a physics-guided dual-stream fusion framework for extreme few-shot cross-domain fault diagnosis. The method does not treat the Laplace wavelet, STFT, CNNs, or AdaBN as newly introduced techniques. Instead, it integrates these components into a unified diagnostic pipeline designed for long-tailed target support sets under large speed shifts. A learnable Laplace wavelet convolution is used in the temporal branch to emphasize transient impact responses, while STFT spectrograms provide a complementary time-frequency representation for the two-dimensional branch. The two feature streams are then fused for target fault classification. For domain adaptation, a Strict AdaBN strategy is applied using only the target support set, rather than the target test data or a large unlabeled target pool. Under the evaluated 50 healthy + 12 fault support condition, the healthy samples provide target-domain operating-background statistics for BN recalibration, while the limited fault samples are used for supervised classifier adjustment. Experiments on the HUSTbearing and Torino DIRG datasets show that the proposed integrated framework achieves stable performance under the evaluated few-shot cross-speed settings. These results suggest that combining physics-guided Laplace convolution, time-frequency representations, and support-set-restricted BN recalibration can be useful for bearing fault diagnosis when target fault samples are limited. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Aiming at the problem of weak fault features and difficult localization of adjacent nodes in distribution networks, we constructed a two-stage cascaded architecture to decouple the diagnosis task into fault classification and section location. The feature layer fuses MS-CNN, SimAM, and Transformer to form a spatio-temporal dual attention mechanism that synchronously captures spatial saliency and global temporal logic. A prototype network is introduced at the fault location decision layer, and metric learning is used to solve the problem of feature aliasing of adjacent nodes. The experimental results show that the accuracy of fault classification and localization are 98.61% and 94.22%, respectively, and it exhibits graceful degradation under extremely low-SNR conditions, which verifies the effectiveness of the proposed strategy in the refined fault diagnosis of distribution networks. Full article

AI, specifically Bayesian networks, was applied in this study to diagnose and predict interruptions (whether due to faults or maintenance) that most significantly impact the time of interruption per kilowatt and frequency of maintenance intervention per kilowatt indicators in electrical distribution systems. Bayesian networks were employed to identify which functional stages, such as sub-transmission lines, distribution substations, and medium-voltage networks, exert the greatest influence on these performance metrics. Additionally, the analysis was conducted to categorize the interruption catalog and contribute to these impacts. By disaggregating the service quality and maintenance indicators reported monthly by the Guayas Los Ríos Business Unit of the National Electricity Corporation to Ecuador’s electricity sector regulatory bodies, the developed framework in this study enhances service reliability, optimizes maintenance planning, and reduces interruption times. Bayesian network models generated using R illustrate relationships between interruption causes and their impact on service quality indicators. Furthermore, a comparison of several models, including Naive Bayes, Tree Augmented Naive Bayes (TAN), and Backward Sequential Elimination and Joining (BSEJ), demonstrated that TAN and BSEJ achieved the highest accuracy in predicting interruption outcomes. These insights allow for more efficient targeting of maintenance resources, ultimately reducing the most impactful categories of interruptions and improving overall technical service quality. Full article

To address fault diagnosis under limited sample conditions, this paper proposes a small-sample diagnosis framework integrating multibody dynamic modeling and a GAN–CNN fusion strategy. First, a rigid–flexible coupled multibody dynamic model of the reducer is established to simulate vibration responses under typical fault modes, including broken gear tooth, gear wear, and bearing outer ring fault, thereby generating representative simulation samples. Second, to reduce the distribution discrepancy between simulated and measured data, the simulated samples are introduced into a generative adversarial learning framework for feature enhancement, with limited measured samples used as references. Cosine similarity is employed to evaluate the consistency between the enhanced simulated data and the measured data in the feature space. Finally, the enhanced simulated samples are fused with measured samples to construct a hybrid dataset for convolutional neural network training and fault classification. Experimental results show that the proposed framework improves the similarity between simulated and measured data, with cosine similarity increasing from below 0.65 to above 0.80. Under small-sample conditions, the mean diagnosis accuracy reaches 83.81%, which is 17.33 percentage points higher than that obtained using the original small-sample dataset. The proposed framework provides an effective modeling and algorithmic approach for reducer fault diagnosis under data-scarce conditions. Full article

Knowledge graph link prediction is a fundamental task for improving the completeness and reasoning capability of knowledge graphs. In industrial knowledge graph scenarios, missing relations may limit knowledge completion, relational reasoning, and downstream industrial applications. Fault diagnosis is a representative application scenario, where missing relations among fault phenomena, alarm information, fault locations, and fault causes may further affect fault analysis, maintenance decision-making, and industrial knowledge services. Industrial knowledge graphs usually suffer from sparse local structures, imbalanced relation distributions, explicit entity-type boundaries, and highly confusing candidate entities with similar structural or semantic contexts. These characteristics make it difficult for conventional embedding-based or graph neural network-based methods to achieve reliable candidate ranking by relying only on structural propagation or semantic matching. To address these challenges, this study proposes a type-constrained structural–semantic fusion framework with dynamic relation priors for industrial knowledge graph link prediction, and further investigates its application to fault diagnosis. The proposed framework extends a relation-centered graph neural reasoning backbone by generating dynamic relation priors through query-conditioned relation-level graph propagation over a predefined relation graph, thereby enhancing query-specific structural reasoning. It further introduces a semantic projection module to align textual representations of entities and relations with structural representations at the candidate-ranking stage. In addition, relation-category and hierarchy-aware signals are used to modulate relation representations during propagation, while entity-type constraints are incorporated into final scoring and type-constrained hard negative construction. In this way, structural evidence, textual semantic information, and entity-type validity constraints are jointly used for candidate ranking rather than being treated as isolated signals. Experiments are conducted on two public benchmark datasets, WN18RR and FB15k-237, and two industrial knowledge graph datasets in Chinese and English. The Chinese industrial knowledge graph is constructed from fault diagnosis knowledge and is used as a representative application dataset, while the English industrial knowledge graph is used to further evaluate the adaptability of the proposed framework in a related industrial production scenario. The proposed method achieves MRR scores of 0.599 and 0.446 on WN18RR and FB15k-237, respectively, and obtains MRR scores of 0.8532 and 0.7994 on the Chinese and English industrial knowledge graphs. The results demonstrate that the proposed framework improves both general link prediction performance and industrial-domain adaptability, especially in scenarios involving sparse structures, type-constrained candidate validity, and semantically confusing entities, and shows practical potential for fault diagnosis applications. 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

The safe operation of power battery systems relies on the dynamic symmetric equilibrium of electrochemical distribution and thermal management states, whereas fault occurrence is often accompanied by symmetry breaking. To achieve accurate fault diagnosis and symmetry restoration, this study proposes a symmetrical closed-loop framework (DBP-KG-FBN) that integrates digital battery passport (DBP) text mining, knowledge graph (KG), and fuzzy Bayesian network (FBN). Power battery fault diagnosis is critical to new energy vehicle (NEV) safety; however, conventional methods face two key limitations: (1) they inadequately exploit multi-source heterogeneous textual data in DBPs; and (2) they fail to handle uncertainty in fault propagation. The methodology proceeds as follows. First, a BERT-BiLSTM-CRF model extracts fault-related entities and relations from unstructured DBP text, which are structured into a Neo4j-based knowledge graph. Second, via rule-based topological mapping, the KG topology is transformed into a Bayesian network through structurally symmetric transformation between the semantic and probabilistic layers, with cyclic dependencies resolved by introducing latent variables. Third, network parameters are determined by integrating fuzzy set theory with game theory-based weighting to quantify uncertainty and subjectivity in expert evaluations, thereby achieving symmetric utilization of subjective and objective information. This enables bidirectional symmetric reasoning for forward fault prediction and backward fault traceability. Experimental results demonstrate that while maintaining symmetric stability of the diagnostic knowledge topology, the proposed DBP-KG-FBN method achieves a diagnostic accuracy of 0.92 (Top-3). This symmetrical closed-loop framework significantly outperforms fault tree analysis (FTA) and event tree analysis (ETA) in diagnostic accuracy and reasoning efficiency. It transforms unstructured DBP data into computable knowledge for intelligent battery diagnosis. Future work will expand the corpus via transfer learning and optimize adaptive weighting algorithms for expert evaluations. Full article

Transformers are core equipment in power systems, and their failure may cause severe accidents. Dissolved gas analysis (DGA) is one of the dominant techniques for fault diagnosis of oil-immersed transformers. To achieve lightweight design and high identification efficiency, this paper proposes an early fault identification method for transformers based on the integration of the Convolutional Block Attention Module-enhanced ShuffleNetV2 (CBAM-SV2) model and Gramian Angular Field (GAF). First, hybrid oversampling is used for data preprocessing. Then, the preprocessed one-dimensional gas data are converted into dual-channel two-dimensional images via GAF as the input of the classification network. Finally, a CBAM-SV2 model integrating deep convolutional networks and attention mechanisms is constructed, which combines the lightweight advantage of ShuffleNetV2 and the powerful feature representation ability of the Convolutional Block Attention Module (CBAM). Feature extraction and classification are performed by the CBAM-SV2 model to output the identification results. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) and a confusion matrix are used to visualize classification performance for intuitive evaluation of the network’s effectiveness. The experimental results show that, compared with other mainstream algorithms, the proposed method achieves higher recognition accuracy in transformer early fault classification under imbalanced data conditions. Full article

A novel method based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) combined with a modified multiple branch convolutional neural network (MMBCNN) is designed to detect fault events in distribution networks and to classify various faults in a distribution system. Given the presence of noise components in transient voltage signals, a moving time window technique integrated with the NA-MEMD method is employed to process high-frequency sampling and long-term series signals. This method is also utilized to reliably identify noise components in modal components through permutation entropy. On this basis, the Clarke transform is employed to convert transient voltage signals into the d–q axis, and three-phase voltage waveforms are transformed into a ring image. Moreover, an MMBCNN is developed to accurately detect and classify distribution network faults, and a modified pooling function is introduced to improve feature extraction ability and model convergence performance. Finally, the accuracy and effectiveness of the proposed algorithm are estimated and analyzed using measurement and fault simulation data from distribution networks. Full article

Accurate and rapid fault diagnosis is critical for active distribution networks characterized by growing structural complexity and diverse load profiles. This paper proposes a two-stage fault diagnosis framework that synergistically combines colored Petri nets (CPN) and fuzzy colored Petri nets (FCPN). In the first stage, a CPN fault zone search model employing a breadth-first search (BFS) strategy is developed to identify suspected faulty components by processing circuit breaker operation information and grid topology. In the second stage, an FCPN diagnosis model is constructed by extending hierarchical fuzzy Petri nets through color assignment to confidence tokens. A key feature of this model is a dedicated initial confidence assessment module that dynamically evaluates the reliability of protection and circuit breaker actions by synthesizing device self-check alarms and operational timing information, thereby overcoming the limitation of empirical, static confidence assignment in existing methods. The resulting initial confidence values are then propagated through a hierarchical confidence inference module to determine the fault likelihood of each suspected component. Comparative simulations across four fault scenarios demonstrate that the proposed method achieves higher diagnostic accuracy and stronger fault tolerance than state-of-the-art approaches, correctly identifying all faulty components even under degraded alarm conditions. Full article

In underground coal mining, the gear of a shearer’s rocker arm endures extreme stress and environmental fluctuations. Failures in this vital component can pose serious safety hazards, cause prolonged operational downtime, and result in significant financial losses. Therefore, accurate gear fault diagnosis is crucial. However, conventional diagnostic methods often struggle with limited feature extraction and poor performance when dealing with non-stationary, noisy signals typical of this environment. To address these challenges, a hybrid model consisting of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Markov Transition Model (MTM) is proposed. In this framework, the CNN is used to extract both global and local features related to gear fault. A time-distributed feature extractor is then integrated with the LSTM to capture the temporal progression of these features, aiding in effective modeling of fault evolution over time. Finally, the MTM further refines classification by incorporating probabilistic state transition between fault conditions, thereby improving diagnostic stability and robustness under noise. Experimental validation was done using vibration data from the Taizhong Coal Machinery rocker arm test platform and gear data from Southeast University and achieved up to 99.79% accuracy. These results show this proposed method outperformed other advanced diagnostic methods, offering dependable fault diagnosis and strong noise resistance even under extreme noise conditions of −5 dB SNR. Full article

Unsupervised domain adaptation (UDA) has been extensively studied for bearing fault diagnosis under multiple operating conditions by mitigating distribution discrepancies across domains. However, in cross-domain imbalanced scenarios, bearing vibration signals are affected by both feature shift and class imbalance. Although a robust decision boundary learned from the source domain is critical for reliable transfer, classifier discriminability and robustness can be degraded by hard samples located near the boundary. As a result, the decision boundary may become ambiguous during adaptation, leading to degraded diagnostic performance in the target domain. To address these issues, a Maximum Margin Local Domain Adaptation (MMLDA) framework is proposed in which a multi-scale convolutional neural network is adopted as the backbone. Three core components are integrated into our framework: first, category-level reweighting to alleviate source-domain class imbalance; second, cross-domain local category alignment to reduce fine-grained feature discrepancies and feature shift; and finally, maximum-margin loss regularization to impose adaptive margin constraints on hard samples for improved decision boundary robustness. To evaluate the proposed method, cross-domain imbalanced transfer tasks under multiple operating conditions were constructed on two public bearing fault datasets, and comparative experiments were conducted. The results under different imbalance protocols demonstrate improved robustness and generalization of MMLDA. Full article

(1) Background: The scarcity and imbalance of real fault data significantly limit the development of data-driven fault diagnosis methods for liquid rocket engines (LREs), especially under few-shot conditions. (2) Methods: To address this issue, this study proposes a GAN-based fault data augmentation framework for multivariate LRE time-series signals and a hybrid diagnostic classifier combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and multi-head attention (MHA). The GAN component is introduced to alleviate fault-data scarcity and class imbalance by generating additional fault-like samples, while the classifier is designed to capture local features, long-range temporal dependencies, and diagnostically informative temporal regions. (3) Results: A multidimensional evaluation based on temporal similarity, statistical consistency, and global distribution discrepancy indicates that the generated samples preserve important characteristics of the original signals under the current evaluation protocol. On the augmented LRE dataset, the proposed classifier achieved strong diagnostic performance. In addition, supplementary experiments on the public HIT aero-engine dataset further support the effectiveness of the classifier architecture, its component-wise contribution, and its behavior under imbalanced few-shot settings, while also demonstrating the value of uncertainty-aware prediction. (4) Conclusions: The results provide encouraging evidence that the proposed framework can improve LRE fault diagnosis under data-scarce conditions. However, the present findings should be interpreted within the scope of the available data and evaluation setting. More comprehensive generator-side ablation, broader external validation, and physics-oriented assessment of the generated signals are still needed before stronger conclusions can be made. Full article