Search Results (112)

The ageing of elevator travelling cables results in the breakage of inner copper strands, leading to communication and control faults in the elevator system. In this paper, a travelling cable state evaluation method based on time-frequency transformation and a deep learning fitting method is proposed. The cable diagnosis is based on the transmission line theory and finite element simulation results, which indicate that the number of broken strands of copper wires in twisted cables is positively related to the amplitude of fluctuation in the cable’s transmission spectrum. To evaluate this fluctuation with low cost and high accuracy, we acquired the 500 Msps time-domain signal after a square wave with different periods was transmitted through the detected cable; the transmission in base frequency and harmonics is calculated and combined into the total transmission spectrum. A deep learning model with a two-layer 1-D CNN and squeeze-excitation channel attention is utilized to fit the spectrum data, and cross-entropy is applied to estimate the departure between the fitting results and the experimental data, which serves as the cable’s broken-state index. Experiments demonstrate that the proposed method is able to detect minor cable faults such as one or two copper strands broken and could distinguish different broken states with a sensitivity of 16.42 ± 1.39 per break strand. Full article

Reciprocating compressors, which serve as core equipment in the petrochemical and natural gas transmission sectors, operate under prolonged variable loads and high-frequency impact conditions. Critical components, such as valves and piston rings, are prone to failure. Existing fault diagnosis methods suffer from inadequate spatio-temporal feature extraction and neglect spatio-temporal correlations. To address this, this paper proposes a spatio-temporal feature fusion-based fault diagnosis method for reciprocating compressors. This method constructs a spatio-temporal feature fusion model (STFFM) comprising three principal modules: First, a spatio-temporal feature extraction module employing a multi-layered stacked bidirectional gated recurrent unit (BiGRU) with batch normalisation to uncover temporal dependencies in long-term sequence data. A graph structure is constructed via k-nearest neighbours (KNN), and an enhanced graph isomorphism network (GIN) is integrated to capture spatial domain fault information variations. Second, the spatio-temporal bidirectional attention-gated fusion module employs a bidirectional multi-head attention mechanism to enhance temporal and spatial features. It incorporates a cross-modal gated update mechanism and learnable weight parameters to dynamically retain the highly discriminative features. Third, the classification output module enhances the model’s generalisation capability through multi-layer fully connected layers and regularisation design. Research findings demonstrate that this approach effectively integrates spatio-temporal coupled fault features, achieving an average accuracy of 99.14% on an experimental dataset. This provides an effective technical pathway for the precise identification of faults in the critical components of reciprocating compressors. Full article

Accurate diagnosis of aero-engine faults and precise signal characterization are crucial to ensuring operational reliability and service life prediction. The structural complexity of engines and the variability of operating conditions pose significant challenges for fault diagnosis and identification. Based on an analysis and emphasis on the critical importance of aero-engine fault signal recognition and diagnosis, this paper comprehensively reviews and discusses the classification and evolution of aero-engine fault signal recognition techniques. The review traces this evolution along its developmental trajectory, from classical methods to emerging approaches such as quantum signal processing for weak feature extraction. It also examines characteristics of different types of aviation engine failures and the progression of diagnostic research over time. This review provides multiple tables to compare the applicability, advantages, and limitations of various signal recognition methods and deep learning diagnostic architectures. Detailed discussions synthesize the relative merits of different approaches and their selection trade-offs. Based on this overview, the paper outlines the complexity of real aero-engine faults and key research directions. Building on these developments in fault signal recognition and diagnosis, the paper addresses the complexity and the research areas receiving particular attention within real aero-engine faults. It highlights key research areas, including handling data imbalance, adapting to variable and cross-domain conditions, and advancing diagnostic and data enhancement methods for weak composite faults. Finally, the paper analyzes the multifaceted challenges in the field and identifies future trends in aero-engine fault signal recognition and intelligent diagnosis. Full article

As a core component of the gearbox, bearings are crucial to the stability and reliability of the transmission system. However, dynamic variations in operating conditions and complex noise interference present limitations for existing fault diagnosis methods in processing non-stationary signals and capturing complex features. To address the aforementioned challenges, this paper proposes a bearing fault diagnosis method based on a multi-feature fusion dual-channel CNN-Transformer-CAM framework. The model cross-fuses the two-dimensional feature images from Gramian Angular Difference Field (GADF) and Generalized S Transform (GST), preserving complete time–frequency domain information. First, a dual-channel parallel convolutional structure is employed to separately sample the generalized S-transform (GST) maps and the Gramian Angular Difference Field (GADF) maps, enriching fault information from different dimensions and effectively enhancing the model’s feature extraction capability. Subsequently, a Transformer structure is introduced at the backend of the convolutional neural network to strengthen the representation and analysis of complex time–frequency features. Finally, a cross-attention mechanism is applied to dynamically adjust features from the two channels, achieving adaptive weighted fusion. Test results demonstrate that under conditions of noise interference, limited samples, and multiple operating states, the proposed method can effectively achieve the accurate assessment of bearing fault conditions. Full article

Cross-condition fault diagnosis of cotton harvester picking-head drivetrains remains challenging due to significant distribution discrepancies in vibration signals under different operating conditions. Existing transfer learning approaches predominantly focus on domain-invariant features while failing to sufficiently exploit domain-specific information and the structural constraints embedded in target-domain normal samples, which often leads to unstable diagnostic performance across conditions. To address this issue, this paper proposes a prototype-guided dual-feature transfer learning method termed Proto-DISFNet (Prototype-guided Domain-Invariant and Domain-Specific Feature Network). The proposed method explicitly disentangles domain-invariant and domain-specific features to alleviate the impact of operating condition variations. High-confidence pseudo-labeled samples, selected through a filtering strategy, are utilized to construct class prototypes in the target domain, thereby enhancing semantic consistency and structural awareness in the feature space. In addition, a stage-wise training strategy is introduced to coordinate multi-task optimization, which improves training stability and overall adaptability under representative complex engineering operating conditions. Experiments conducted on three vibration datasets, JNU, THU, and CHPH-FETB, demonstrate that Proto-DISFNet achieves stable and competitive cross-condition diagnostic performance under varying degrees of domain shift and operating conditions. These results indicate the engineering relevance and potential applicability of the proposed method for fault diagnosis of cotton harvester picking-head drivetrains. Full article

To address the limited performance of transfer fault diagnosis for planetary gearboxes under cross-operating conditions, which is caused by the heterogeneous feature distribution of vibration data and insufficient feature extraction. An improved domain-adversarial neural network (IDANN) model based on a joint-adaptive-domain alignment component and a dual-branch feature extractor is proposed. Firstly, a joint domain adaptation alignment approach, integrating maximum mean discrepancy (MMD) and CORrelation ALignment (CORAL), is proposed to realize the correlation structure matching of features between the source and target domains of IDANN. Secondly, a dual-branch feature extractor composed of ResNet18 and Swin Transformer is proposed with an attention-weighted fusion mechanism to enhance feature extraction. Finally, validation experiments conducted on public planetary gearbox fault datasets show that the proposed method attains high accuracy and stable performance in cross-operating-condition transfer fault diagnosis. Full article

To address the challenges of fault diagnosis in wind turbine planetary gearboxes under strong noise and limited labeled target-domain data, this paper proposes a novel intelligent diagnostic method integrating multi-source feature fusion with domain adaptation transfer learning. A Multi-source Feature Attention Fusion Convolutional Neural Network (MSFAF-CNN) is constructed, which dynamically fuses vibration signals from multiple measurement points using a channel attention mechanism to assign optimal weights to the most discriminative features. Furthermore, an improved Multi-source Local Maximum Mean Discrepancy (MS-LMMD) loss is introduced, establishing a hierarchical domain adaptation framework that enables fine-grained alignment of feature distributions between the labeled source and unlabeled target domains. Experimental results under the challenging condition of −4 dB noise demonstrate the superiority of the proposed approach: the cross-condition transfer task (A→B) achieves an accuracy of 95.32%, outperforming the conventional LMMD method by 1.05%. Finally, t-SNE-based visualization confirms that the method enhances cross-domain feature compactness, enabling direct processing of raw vibration signals without manual feature extraction. The findings indicate that the proposed approach offers a highly robust solution for fault diagnosis in drive systems under low signal-to-noise ratios and unlabeled operating conditions. Full article

Fault diagnosis of rolling bearings is crucial for ensuring the maintenance and reliability of industrial equipment. Existing cross-domain diagnostic methods often struggle to maintain performance under evolving mechanical and environmental conditions. This limits their robustness in long-term real-world deployments. To address this, we propose a novel online learning framework that continuously adapts to distribution shifts using streaming vibration data. Specifically, the proposed framework consists of three core modules: the Feature Extraction Module that encodes raw vibration signals into low-dimensional latent representations; the Fault Sample Generation Module (comprising a generator and discriminator network) that synthesizes diverse fault samples conditioned on normal-condition data; and the Classification Module that incrementally adapts by leveraging both synthesized fault samples and streaming normal-condition signals. We also introduce a domain-shift indicator ScoreODS to dynamically control the transition between prediction and fine-tuning phases during deployment. Extensive experiments on both public and private datasets demonstrate that the proposed method outperforms the most competitive method, achieving about a 4% improvement in diagnostic accuracy and enhanced robustness for long-term fault diagnosis under distribution shifts. Full article

Reliable bearing fault diagnosis plays an important role in maintaining the safety and performance of rotating machinery in industrial systems. Although deep learning models have achieved remarkable success in this field, their dependence on a single feature-extraction approach often restricts the diversity of learned representations and limits diagnostic accuracy. To overcome this limitation, this study proposes an attention-guided dual-path framework that integrates spatial and time–frequency feature learning with transformer-based classification for precise fault identification. In the proposed framework, vibration signals collected from an experimental bearing test rig are simultaneously processed through two complementary pipelines: one converts the signals into two-dimensional matrix images to extract spatial features, while the other transforms them into continuous wavelet transform (CWT) scalograms to capture fine-grained temporal and spectral information. The extracted features are fused through a lightweight transformer encoder with an attention mechanism that dynamically emphasizes the most informative representations. This fusion enables the model to effectively capture cross-domain dependencies and enhance discriminative capability. Experimental validation on an industrial vibration dataset demonstrates that the proposed model achieves 99.87% classification accuracy, outperforming conventional CNN and transformer-based approaches. The results confirm that integrating multi-domain features with attention-driven fusion significantly improves the robustness and generalization of deep learning models for intelligent bearing fault diagnosis. Full article

Aiming to tackle the challenge of feature transfer in cross-domain fault diagnosis for rolling bearings, an enhanced domain adaptation-based intelligent fault diagnosis method is proposed. This method systematically combines multi-layer multi-core MMD with adversarial domain classification. Specifically, we will extend alignment to multiple network layers, while previous work typically applied MMD to fewer layers or used single core variants. Initially, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract features from both the source and target domains, thereby enhancing the diagnostic model’s cross-domain adaptability through shared feature learning. Subsequently, to address the distribution differences in feature extraction, the multi-layer multi-kernel maximum mean discrepancy (ML-MK MMD) method is employed to quantify the distribution disparity between the source and target domain features, with the objective of extracting domain-invariant features. Moreover, to further mitigate domain shift, a novel loss function is developed by integrating ML-MK MMD with a domain classifier loss, which optimizes the alignment of feature distributions between the two domains. Ultimately, testing on target domain samples demonstrates that the proposed method effectively extracts domain-invariant features, significantly reduces the distribution gap between the source and target domains, and thereby enhances cross-domain diagnostic performance. Full article

Reliable bearing fault diagnosis across diverse operating conditions remains a fundamental challenge in intelligent maintenance. Traditional data-driven models often struggle to generalize due to the limited ability to represent complex and heterogeneous feature relationships. To address this issue, this paper presents an Adaptive Multi-view Hypergraph Learning (AMH) framework for cross-condition bearing fault diagnosis. The proposed approach first constructs multiple feature views from time-domain, frequency-domain, and time–frequency representations to capture complementary diagnostic information. Within each view, an adaptive hyperedge generation strategy is introduced to dynamically model high-order correlations by jointly considering feature similarity and operating condition relevance. The resulting hypergraph embeddings are then integrated through an attention-based fusion module that adaptively emphasizes the most informative views for fault classification. Extensive experiments on the Case Western Reserve University and Ottawa bearing datasets demonstrate that AMH consistently outperforms conventional graph-based and deep learning baselines in terms of classification precision, recall, and F1-score under cross-condition settings. The ablation studies further confirm the importance of adaptive hyperedge construction and attention-guided multi-view fusion in improving robustness and generalization. These results highlight the strong potential of the proposed framework for practical intelligent fault diagnosis in complex industrial environments. Full article

Domain generalization (DG) aims to develop models that perform robustly on unseen target domains, a critical but challenging objective for real-world fault diagnosis. The challenge is further complicated in compound fault diagnosis, where the rigidity of hard labels and the simplicity of label smoothing under-represent inter-class relations and compositional structures, degrading cross-domain robustness. While current domain generalization methods can alleviate these issues, they typically rely on multi-source domain data. However, considering the limitations of equipment operational conditions and data acquisition costs in industrial applications, only one or two independently distributed source datasets are typically available. In this work, an adaptive label refinement network (ALRN) was designed for learning with imperfect labels under source-scarce conditions. Compared to hard labels and label smoothing, ALRN learns richer, more robust soft labels that encode the semantic similarities between fault classes. The model first trains a convolutional neural network (CNN) to obtain initial class probabilities. It then iteratively refines the training labels by computing a weighted average of predictions within each class, using the sample-wise cross-entropy loss as an adaptive weighting factor. Furthermore, a label refinement stability coefficient based on the max-min Kullback–Leibler (KL) divergence ratio across classes is proposed to evaluate label quality and determine when to terminate the refinement iterations. With only one or two source domains for training, ALRN achieves accuracy gains exceeding 22% under unseen operating conditions compared with a conventional CNN baseline. These results validate that the proposed label refinement algorithm can effectively enhance the cross-domain diagnostic performance, providing a novel and practical solution for learning with imperfect supervision in cross-domain compound fault diagnosis. Full article

Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management of bearings has become an important issue. Although deep learning has shown remarkable advantages, its performance still relies on the assumption that the training and testing data share the same distribution, which often deteriorates in real applications due to variations in load and rotational speed. This study focused on the scenario of domain generalization (DG) and proposed a Meta-Learning with Gradient Alignment and Data Augmentation (MGADA) method for cross-domain bearing fault diagnosis. Within the meta-learning framework, Mixup-based data augmentation was performed on the support set in the inner loop to alleviate overfitting under small-sample conditions and enhanced task-level data diversity. In the outer loop optimization stage, an arithmetic gradient alignment constraint was introduced to ensure consistent update directions across different source domains, thereby reducing cross-domain optimization conflicts. Meanwhile, a centroid convergence constraint was incorporated to enforce samples of the same class from different domains to converge to a shared centroid in the feature space, thus enhancing intra-class compactness and semantic consistency. Cross-working-condition experiments conducted on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves high classification accuracy across different target domains, with an average accuracy of 98.89%. Furthermore, ablation studies confirm the necessity of each module (Mixup, gradient alignment, and centroid convergence), while t-SNE and confusion matrix visualizations further illustrate that the proposed approach effectively achieves cross-domain feature alignment and intra-class aggregation. The proposed method provides an efficient and robust solution for bearing fault diagnosis under complex working conditions and offers new insights and theoretical references for promoting domain generalization in practical industrial applications. Full article

Accurate bearing fault diagnosis under various operational conditions presents significant challenges, mainly due to the limited availability of labeled data and the domain mismatches across different operating environments. In this study, an adaptive meta-learning framework (AdaMETA) is proposed, which combines dynamic task-aware model-independent meta-learning (DT-MAML) with efficient multi-scale attention (EMA) modules to enhance the model’s ability to generalize and improve diagnostic performance in small-sample bearing fault diagnosis across different load scenarios. Specifically, a hierarchical encoder equipped with C-EMA is introduced to effectively capture multi-scale fault features from vibration signals, greatly improving feature extraction under constrained data conditions. Furthermore, DT-MAML dynamically adjusts the inner-loop learning rate based on task complexity, promoting efficient adaptation to diverse tasks and mitigating domain bias. Comprehensive experimental evaluations on the CWRU bearing dataset, conducted under carefully designed cross-domain scenarios, demonstrate that AdaMETA achieves superior accuracy (up to 99.26%) and robustness compared to traditional meta-learning and classical diagnostic methods. Additional ablation studies and noise interference experiments further validate the substantial contribution of the EMA module and the dynamic learning rate components. Full article

To gain a comprehensive understanding of the current research landscape in the field of new energy vehicle (NEV) fires and to explore its knowledge base and emerging trends, bibliometric methods—such as co-occurrence, clustering, and co-citation analyses—were employed to examine the relevant literature. A research knowledge framework was established, encompassing four primary themes: thermal management and performance optimization of power batteries, battery materials and their safety characteristics, thermal runaway (TR) and fire risk assessment, and fire prevention and control strategies. The key research frontiers in this domain could be classified into five categories: mechanisms and propagation of TR, development of high-safety battery materials and flame-retardant technologies, thermal management and thermal safety control, intelligent early warning and fault diagnosis, and fire suppression and firefighting techniques. The focus of research has gradually shifted from passive identification of causes and failure mechanisms to proactive approaches involving thermal control, predictive alerts, and integrated system-level fire safety solutions. As the field advances, increasing complexity and interdisciplinary integration have emerged as defining trends. Future research is expected to benefit from broader cross-disciplinary collaboration. These findings provide a valuable reference for researchers seeking a rapid overview of the evolving landscape of NEV fire-related studies. Full article