Search Results (4,825)

Underground duct cables are widely used in urban power distribution systems, but their enclosed installation environment makes defect inspection difficult, labor-intensive, and potentially hazardous. Infrared thermography can capture abnormal temperature distributions caused by insulation degradation, conductor damage, sheath failure, or severe structural defects, while robot-based inspection provides a promising solution for confined duct environments. However, thermographic fault detection for underground small-diameter duct cables remains insufficiently studied, and practical deployment requires lightweight models suitable for embedded edge devices. In this study, an improved YOLO26-based thermographic fault detection framework is proposed for underground duct cable inspection. A Cable-Thermo dataset is constructed using an ANSYS 2025 R2-based thermoelectric coupling simulation, covering four defect categories: hollow-type damage, conductor burnout, sheath damage, and severe damage. To balance detection accuracy and deployment efficiency, two model variants are developed. YOLO26-Thermo-E retains the original detection scales and integrates CDA and SimSPPF modules for accuracy-prioritized diagnosis. YOLO26-Thermo-H further removes the small-scale detection branch as a deployment-oriented design choice, based on the scale distribution observed in the simulation dataset, where most fault-induced thermal anomalies appear as spatially continuous medium- or large-scale regions. This design assumption still requires further validation using real duct thermographic data. Experiments show that YOLO26-Thermo-E achieves the highest mAP50 of 99.20%. YOLO26-Thermo-H maintains a mAP50 of 99.00% while reducing GFLOPs by 34.3% and parameters by 16.2% compared with YOLO26. On an NVIDIA Jetson Orin NX, YOLO26-Thermo-H reaches 34 FPS under FP16 inference and 45 FPS under INT8 inference. These results demonstrate the feasibility of the proposed framework under controlled simulation conditions and its potential for edge deployment. The limitations of the simulation-based dataset are also discussed, and future work will focus on real-scene data collection and simulation-to-real generalization. Full article

With the continuous advancement of industrial intelligence, the application of hydraulic robots is becoming increasingly widespread, and the demand for their health diagnosis and maintenance is becoming more urgent. By integrating digital twin (DT) and deep learning technologies, this paper presents an intelligent fault diagnosis method for hydraulic robots based on multi-source information fusion. Firstly, a fault diagnosis architecture and solution for hydraulic robots based on DT technology are proposed. Secondly, a DT model of the hydraulic robot, which incorporates a 3D model and an attribute model with virtual–physical synchronization capabilities, is established, and a calibration method for the twin model is explored. Next, for four typical faults—leakage in the hydraulic system, valve sticking, damping hole blockage, and filter blockage—fault mechanism analysis and evolution process simulation are conducted on the established DT model. A multi-source high-quality dataset, covering normal operating conditions and multiple fault scenarios, is constructed to drive the data twin model. Finally, a feature extraction method combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanisms is proposed. This is followed by using a Random Forest (RF) classifier to achieve accurate fault diagnosis for various hydraulic system failures. The experimental results validate the effectiveness and practicality of this method. Full article

Rolling bearing fault diagnosis is essential for ensuring the safety and reliability of rotating machinery. Although deep learning-based methods have achieved promising performance, they usually require sufficient labeled data, which is difficult to obtain in practical industrial scenarios where fault samples are scarce and data sharing across sites is restricted by privacy and confidentiality constraints. Federated learning enables collaborative model training without transmitting raw data, but existing federated fault diagnosis methods often degrade under few-shot conditions. Moreover, current federated meta-learning approaches mainly focus on model-level adaptation and lack explicit class-level representation alignment, leading to prototype drift across heterogeneous operating conditions. To address these challenges, this paper proposes ProtoFed, a prototype-enhanced federated meta-learning framework for few-shot rolling bearing fault diagnosis. ProtoFed converts raw vibration signals into time–frequency representations using continuous wavelet transform and performs local episodic learning with prototypical networks. A Global Prototype Calibration mechanism aggregates local class prototypes into stable global prototypes with exponential moving average smoothing, while a Prototype-Distance Aware Aggregation strategy adaptively adjusts client aggregation weights according to local–global prototype divergence. Experiments on the CWRU and Paderborn University bearing datasets under non-IID 5-shot and 10-shot settings show that ProtoFed consistently outperforms standard federated learning, prototype-based federated learning, and federated meta-learning baselines. Under the 5-shot setting, ProtoFed achieves 95.63% and 91.35% accuracy on CWRU and PU, respectively, approaching centralized few-shot upper-bound performance while preserving the federated training paradigm. Full article

Cross-noise fault diagnosis remains challenging due to the mismatch between training and testing noise conditions, which degrades feature reliability and model generalization. To address this issue, this paper proposes a reliability-guided adaptive feature fusion framework (RGAF-Net). The method focuses on sample-wise adaptive feature fusion, where the enhanced wide first-layer convolutional neural network(WDCNN) backbone is employed to improve multi-scale feature extraction under noisy environments. In addition, a dual-path architecture is introduced to provide complementary representations, including globally robust structural representations and locally detail-sensitive structural responses. Furthermore, a lightweight reliability estimation module is designed to characterize the signal degradation tendency under noisy conditions of each input sample, based on which a sample-wise routing mechanism dynamically adjusts feature contributions during feature fusion. Experiments on two public bearing datasets (PU and JNU) under cross-noise settings demonstrate that the proposed method achieves improved performance compared with representative approaches, particularly under severe noise conditions. For example, on the JNU dataset at −10 dB, the proposed method improves the Macro-F1 score by over 19 percentage points compared with the baseline WDCNN. Ablation studies and visualization analyses further demonstrate the effectiveness and adaptive fusion behavior of the proposed framework. The results indicate that the proposed method provides an effective solution for robust fault diagnosis under noise mismatch scenarios. Full article

The health status of rolling bearings is critical to the normal operation of rotating machinery. To effectively extract vibration signal features and accurately identify different fault types, a novel method based on enhanced composite multi-scale slope entropy (ECMSE) and a honey badger algorithm-optimized kernel extreme learning machine (HBA–KELM) is proposed. Specifically, ECMSE integrates high-order differences into the composite multi-scale framework to capture high-frequency information while preserving low-frequency characteristics, thereby enhancing the discriminability of time-series representations. Meanwhile, an average coarse-graining strategy is incorporated to achieve a more comprehensive characterization of the signals. The extracted features are then input into the HBA–KELM classifier for fault identification. Experiments conducted on two public and private rolling bearing datasets demonstrate that our method achieves superior performance in distinguishing different fault types and damage levels compared with several existing approaches. Full article

In-flight propulsion system diagnosis in multirotor unmanned aerial vehicles (UAVs) remains a challenging problem due to closed-loop control interactions, strong environmental disturbances, and common-mode effects that obscure rotor-specific anomalies. Conventional passive monitoring approaches based solely on electrical or mechanical measurements are often insufficient for reliable fault localization and for distinguishing global degradations from nominal operation. This paper proposes an active diagnostic framework that exploits low-amplitude sinusoidal excitation injected into the control null space during hover operation. By employing lock-in detection, rotor responses are selectively extracted at the excitation frequency, enabling the derivation of robust amplitude-based sensitivity indicators from rotational speed, current, and electrical power signals. A pairwise signed diagnostic metric is formulated to achieve reliable localization of asymmetric rotor faults. In addition, an absolute indicator referenced to a baseline condition is introduced to capture symmetric degradations affecting all rotors through the combined use of current- and power-based sensitivities. The proposed method is validated in a high-fidelity quadrotor simulation environment incorporating viscous-friction and thrust-coefficient degradation faults. Extensive Monte Carlo analyses demonstrate robust fault-detection and localization performance, including scenarios that are indistinguishable using conventional pairwise normalization techniques. Full article

This paper proposes a novel rolling bearing fault diagnosis method to address the difficulty of accurate feature extraction from nonlinear and non-stationary vibration signals. First, a Levy–Cauchy Optimized Sparrow Search Algorithm (LOCSSA) is developed to optimize the two core parameters (decomposition level and penalty factor) of Variational Mode Decomposition (VMD), and the optimized VMD is used to decompose raw vibration signals to obtain optimal intrinsic mode functions (IMFs). Second, the extracted IMF features are fed into a convolutional neural network (CNN) for local pattern extraction, followed by a bidirectional long short-term memory (BiLSTM) network to model temporal dependencies, with the final fault classification completed via a fully connected layer. Comparative experiments and ablation studies with five benchmark models are conducted to verify the effectiveness of the proposed framework. The results show that the proposed method achieves 96.33% accuracy, 96.67% recall, and 96.54% F1-score, outperforming all benchmark models. Ablation analysis confirms that both LOCSSA-optimized VMD and BiLSTM contribute significantly to performance improvement (p < 0.05), validating the rationality of the proposed method. Full article

The paper focuses on fault diagnosis in nonlinear analog circuits. Due to its complexity, the problem, which has been the subject of research for over 50 years, remains unsolved. The paper proposes a comprehensive method for identifying single hard and soft faults in nonlinear DC circuits. The dictionary-based method belongs to the simulation-before-test method group. During dictionary creation, the possibility of multiple equilibrium points in transistor circuits is considered. For this purpose, Monte Carlo simulations that account for the spread of circuit parameters within tolerance limits are preceded by circuit analyses using a method that guarantees the determination of all operating points. A method for selecting nodes for hard faults is proposed. Two dictionaries are formulated for soft and hard faults. After performing measurements in the circuit under test, the dictionaries are searched to determine the fault type. In the case of soft faults, unlike methods that use artificial intelligence tools, it is possible not only to identify the faulty element but also to determine its approximate range of values. Furthermore, the paper highlights several problems and risks at the dictionary-creation stage. The method was verified through simulation and on a test bench under various test conditions. Full article

Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while conventional methods focusing on global statistical matching usually neglect local structures, leading to confirmation bias under dynamic loads. To improve diagnostic reliability, we propose a Noise-Resilient Whitened Domain Adaptation (NRWDA) framework. To handle covariance fluctuations caused by changing working conditions, a Lipschitz-bounded Temporal Whitening (LTW) module is designed as a low-pass filter. An Entropy-guided Prototype Truncation (EPT) mechanism is adopted to discard ambiguous labels and better calibrate semantic centers. In addition, a Dispersion-Adaptive Contrastive Sharpening (DACS) strategy is introduced to dynamically adjust the contrastive temperature based on predictive dispersion, thus tightening decision boundaries. The proposed method is evaluated on CWRU, PU, and MFPT datasets. The PU dataset, featuring fluctuating loads and non-stationary signals, poses a strict test, yet our model maintains its stability even at a 0 dB SNR—a condition where standard approaches usually break down. During the $P0\to P3$ transfer task involving substantial radial force variations, NRWDA secures a 72.36% accuracy and surpasses established baselines. These findings confirm that our technique successfully isolates dependable diagnostic features from corrupted sensor measurements within actual industrial settings. Full article

Lithium-ion battery energy storage systems (BESSs) face significant safety challenges arising from internal short-circuit (ISC) faults, which can ultimately trigger thermal runaway. To address this, this paper proposes an ISC fault diagnosis method based on multivariate information entropy (MIE). The proposed approach fuses voltage and temperature time series from battery cells to extract fault features via MIE. Furthermore, a hierarchical diagnosis framework incorporating statistical confidence intervals is developed to enable robust ISC fault diagnosis. Experiments were conducted on 180 Ah lithium iron phosphate batteries, utilizing external resistors to simulate ISC faults of varying severity. The method was further validated using real-world fault data from an electric vehicle accident. Results demonstrate that the proposed method effectively distinguishes between normal and faulty cells, with MIE values exhibiting a monotonic increase as fault severity intensifies. In the real-world dataset, the method identifies the faulty cell 240 s before a discernible voltage drop, demonstrating its capability for early ISC detection. Full article

Gearbox fault diagnosis under controlled bench-test conditions with known speed variations and noise interference remains challenging because nonstationarity, background noise, and operating-condition fluctuations can easily submerge weak localized fault features. To address this issue, this study proposes a fault diagnosis method based on a fractional-order-enhanced dual-view representation and VibrMamba–VMamba collaborative modeling. First, this study introduces a Grünwald–Letnikov fractional-order differential enhancement module with a fractional order of $\alpha =0.6$ to strengthen fault-sensitive impulsive components and improve the representation of nonstationary vibration signals. The framework then uses the enhanced signal to construct dual-view inputs: a fractional-order-enhanced one-dimensional vibration sequence and a fractional-order-enhanced synchrosqueezing transform (SST) time–frequency image. Subsequently, the framework constructs a VibrMamba temporal branch and a VMamba visual branch to extract dynamic temporal features and global structural features, respectively. Instead of using simple feature concatenation, this study designs a sample-adaptive collaborative fusion mechanism with gated weighting and cross-branch residual enhancement to integrate complementary temporal–visual representations. Bench-level experiments show that the proposed method achieves 98.90% diagnostic accuracy under clean test conditions and maintains 91.52% accuracy at −5 dB signal-to-noise ratio (SNR). These results should be interpreted as bench-level validation under controlled laboratory conditions rather than as direct evidence of field-level generalization. This framework provides a methodological solution that integrates fractional-order signal enhancement, dual-view representation, and Mamba-style collaborative state-space modeling for gearbox fault classification under controlled laboratory conditions with known speed variations and noise disturbances. 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

Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on wind energy, hybrid energy systems, energy storage, and intelligent energy management. A systematic literature review covering peer-reviewed publications from 2021 to 2025 was conducted, resulting in the analysis of 138 high-quality journal and conference studies. The reviewed studies were categorized according to evolutionary algorithm-based hybrid models, classical neural networks, and deep learning architectures, including Convolutional Neural Network (CNN), LSTMs, GRUs, and attention-based models. The analysis demonstrates that hybrid ML–metaheuristic frameworks significantly enhance forecasting accuracy, system reliability, fault diagnosis, and multi-objective optimization compared to traditional methods. These intelligent approaches directly contribute to Sustainable Development Goals SDG-7 (Affordable and Clean Energy), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-13 (Climate Action). Key challenges and future research directions are discussed, highlighting the need for scalable, explainable, and real-time ML solutions to enable resilient, low-carbon, and sustainable energy systems. Full article

Accurate crack severity classification in rotating shafts remains a challenging task due to the strong spectral overlap between adjacent damage levels and the absence of distinct fault-specific frequency components. In such conditions, conventional vibration-based approaches relying on global spectral descriptors often fail to provide sufficient discriminatory information. This work proposes a stability-driven multi-resolution framework for crack severity classification based on the Wavelet Packet Transform (WPT). The approach aims to identify frequency bands that exhibit consistent diagnostic relevance across multiple decomposition levels while maintaining a monotonic relationship with crack severity. To this end, an interpretability-driven analysis based on Random Forest feature importance is combined with a frequency stability criterion and a monotonicity constraint, enabling the selection of physically meaningful and consistent spectral regions. The proposed framework has been evaluated on vibration data acquired from a rotating shaft test bench under multiple operating speeds and damage conditions. The results have shown that crack progression is characterised by distributed energy variations across specific frequency regions rather than by the emergence of isolated spectral peaks. It can be concluded that the proposed stability-driven band selection approach enables the identification of these regions in a consistent manner across spectral resolutions and operating conditions. Furthermore, the integration of WPT-based features with conventional time- and frequency-domain descriptors leads to a hybrid multi-scale representation that improves classification performance, particularly in intermediate severity regimes where spectral overlap is most pronounced. Overall, the proposed methodology provides a physically interpretable and consistent framework for vibration-based crack severity classification, with potential applicability to a wide range of rotating machinery diagnostics problems. Full article

Railway turnout systems are important components of railway signaling infrastructure, and timely fault diagnosis is essential for ensuring operational safety and maintenance efficiency. In practical applications, turnout fault diagnosis based on power data is often challenged by limited fault samples and severe class imbalance. To address these issues, this paper proposes a DLeNN-Attention-based fault diagnosis method for railway turnout power data, where DLeNN-Attention denotes Dilated-LeNet5-Attention. First, the original power sequences are standardized to a unified length through truncation, zero-padding, and normalization. Then, a hybrid data augmentation strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) and a generative adversarial network (GAN) is adopted to enrich minority fault samples and alleviate class imbalance. Based on the augmented data, a DLeNN-Attention model is designed by integrating dilated convolution with the Convolutional Block Attention Module (CBAM), so as to capture richer temporal characteristics and enhance discriminative fault-related information. In this way, the proposed method can effectively learn representative features from turnout power data and improve fault classification performance. Experimental results on S700K turnout power data demonstrate that the proposed method achieves better diagnosis performance than several baseline models. The results indicate that the proposed method is effective for turnout fault diagnosis under limited-data conditions and shows promising application potential in intelligent health monitoring of railway turnout systems. Full article