Search Results (27)

Underground cables are critical infrastructure for electrical power and communication transmission, and their reliable operation is of paramount importance to urban public safety. Although Distributed Acoustic Sensing (DAS) enables wide-range, continuous, and real-time monitoring, traditional DAS signal processing methods suffer from poor intrusion discrimination and weak anti-interference capability. To address these limitations, we propose a dual-branch network based on multi-domain feature fusion, integrating a Multilayer Perceptron, a Long Short-Term Memory network (LSTM), and an attention mechanism. Vibration signals corresponding to four representative high-risk intrusion events were acquired through controlled field experiments, and a standardized, category-balanced dataset was constructed accordingly. Time-domain, frequency-domain and joint time-frequency features were extracted and mapped through a time-frequency weighting transformation to form one branch of the network, while the parallel branch employed an LSTM to capture long-range temporal dependencies. A multi-head attention mechanism enables deep adaptive fusion of two types of modal information and overcomes the limitations of conventional simple feature concatenation. Comparative experiments against KNN, 1D-CNN and LSTM baselines demonstrate that the proposed model achieves a test accuracy of 98.89%, outperforming all reference methods. Ablation studies further validate the necessity and effectiveness of each constituent module within the proposed architecture. The results indicate that this approach provides reliable support for DAS-based online monitoring of power cables against external damage. Full article

Multi-source information fusion is regarded as a key approach for improving bearing fault diagnosis. However, due to the heterogeneity of multi-source information, asymmetric information contributions, and imbalanced discriminative features, negative transfer may occur during fusion. To address this issue, this paper proposes a negative-transfer-suppression diagnosis framework based on physical-information guidance and adversarially disentangled representation. First, an adaptive preprocessing mechanism guided by acoustic–vibration cross-correlation and mutual information entropy is constructed to extract intrinsic cross-modal correlations, enabling source-end feature reconstruction and commonality enhancement. Second, an attention-based spatial feature extraction operator and an adversarial common-domain representation model are developed to suppress modality-specific interference and disentangle cross-modal shared features. On this basis, sparse coding is employed to fuse common-domain and modality-specific features. Furthermore, a classification effectiveness evaluation index based on fuzzy clustering is introduced into the loss function to dynamically constrain sparse coding weights, thereby reducing interference features and suppressing negative transfer under strong-noise conditions. Experimental results demonstrate that the proposed method effectively achieves the design objective that “fusion outperforms non-fusion,” exhibiting strong noise robustness and high diagnostic accuracy under complex operating conditions. Full article

In industrial equipment fault diagnosis, vibration and acoustic signals are highly complementary yet exhibit significant differences in frequency distribution and noise sensitivity. Traditional multimodal methods generally rely on homogeneous feature extractors and direct feature concatenation, which may fail to capture modality-specific characteristics and introduce irrelevant information during fusion. To address this, we propose a novel multimodal heterogeneous convolutional neural network framework. Specifically, separate 1D CNN branches are designed for vibration and acoustic signals. Their architectural differences are determined by the characteristics of each sensing modality. The vibration branch focuses on extracting high-level discriminative fault features, including impulse responses and modulated components from vibration signals, while the acoustic branch is designed to preserve fragile high-frequency details of acoustic signals. Furthermore, an adaptive cross-attention fusion module is introduced to dynamically model cross-modal dependencies, assigning Softmax-based weights to enhance dominant features and suppress noise. Experiments based on bearing fault experimental data demonstrate that the proposed heterogeneous architecture significantly outperforms traditional homogeneous models. The dynamic weighting mechanism effectively prevents inferior noisy modalities from degrading overall performance, achieving high diagnostic accuracy. Although validated on rolling bearing fault diagnosis, the proposed heterogeneous multimodal framework is not restricted to bearings and can be readily extended to other intelligent condition monitoring tasks involving heterogeneous sensor fusion, such as gearboxes, motors, and other rotating machinery. Full article

Combine harvesters operate under harsh, time-varying field conditions, where unplanned downtime causes significant timeliness losses. This review systematically examines advances in structural fault diagnosis for combine harvesters, tracing the evolution from passive structural redundancy to active state perception and simulation-driven health management. Following a systematic search and screening methodology, the review analyzes the boundaries of structural optimization under fluctuating field conditions, evaluates traditional machine learning (ML) methods with handcrafted features, and surveys deep learning (DL) and multi-sensor fusion advances across vibration, acoustic, and visual modalities. A structured comparison across feature learning, generalization, diagnostic coverage, computational cost, and interpretability highlights the complementary strengths of traditional ML and DL paradigms. Key deployment challenges are identified: weak fault features under strong field noise, data distribution shift under multi-condition coupling, extreme sample scarcity and class imbalance, limited onboard computing and real-time latency constraints, interpretability gaps, hydraulic and pneumatic diagnostic neglect, functional safety compliance (ISO 25119), and the heightened reliability demands of unmanned autonomous operation. To address these challenges, future directions include physics-informed hybrid models, self-supervised pre-training and few-shot learning, lightweight edge inference, staged pre-deployment verification, model updating and lifelong learning strategies, cross-energy-domain diagnosis with fault-tolerant control, human-factors-aware interface design, and digital twin (DT) augmentation—the latter regarded as a strategic research vision requiring incremental validation rather than a near-term deployable solution. This review provides a reference for enhancing combine harvester mission reliability through the integration of AI-enabled perception, multi-source fusion, and simulation-driven health management. Full article

Tool wear directly affects surface quality, dimensional accuracy, and manufacturing cost in milling operations, making reliable wear state classification essential for process control. This paper presents an offline deep learning framework for multiclass tool wear classification using the UC Berkeley milling dataset (NASA-Ames). Statistical features are extracted from vibration, acoustic emission, and spindle motor current signals, and dimensionality is reduced from 78 to 9 informative variables using LASSO regression. A four-layer Long Short-Term Memory (LSTM) network then models the temporal evolution of tool degradation across three wear states: healthy, degraded, and failed. Two model variants are compared: Model A uses sensor-derived features only, while Model B additionally incorporates feed rate and depth of cut as inputs. To prevent data leakage, partitioning is performed at the machining-case level rather than at the individual window level. Model A achieves 92% classification accuracy; Model B reaches 95%, demonstrating that cutting conditions provide contextual information that resolves ambiguity between wear states produced under different machining regimes. These results confirm that combining multisensor feature fusion, LASSO-based selection, and sequential deep learning constitutes an effective framework for tool wear classification in milling. Full article

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

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Unexpected degradation in servomotor-driven multi-mode industrial systems such as CNC feed drives and robotic machining cells compromises positioning accuracy, availability and operational safety, rendering early fault diagnosis and predictive maintenance essential in smart manufacturing environments. In this study, a predictive maintenance framework based on multi-sensor data fusion was developed to support condition monitoring, fault classification, and remaining useful life estimation of robot servomotors. Time- and frequency-domain features were extracted from synchronized electrical current, vibration, acoustic, and temperature signals using fixed-length sliding windows. Feature-level fusion was applied to combine complementary information from different sensor modalities. A data-driven health assessment approach was employed in which an autoencoder model trained on healthy operating data was used to generate a scalar Servomotor Health Score representing degradation progression. Fault types were identified using a Random Forest classifier, while remaining useful life was estimated in terms of operational cycles using a Gradient Boosting regression model. Experimental evaluations were carried out under repeated reference motion profiles, and representative mechanical and electrical fault conditions were introduced in a controlled manner. The results demonstrated that the proposed health score provided a smooth and monotonic degradation trend, enabling early fault detection without false alarms under healthy conditions. High classification performance was achieved for fault identification, and remaining useful life predictions showed low estimation error on previously unseen faulty servomotors. Feature contribution analysis indicated that electrical current and temperature signals provided the most robust indicators of degradation, while vibration and acoustic measurements offered complementary diagnostic information. The proposed framework was shown to be an effective and practical solution for predictive maintenance of servomotor-driven manufacturing systems such as CNC axes and robotic machining platforms operating under low-speed and variable-load conditions. Full article

Accurate fault diagnosis of mining hoisting head sheave systems is critical for ensuring operational safety in harsh underground environments. This study proposes a multi-source fault diagnosis framework that fuses vibration and acoustic information using a Convolutional Neural Network and Random Forest (CNN-RF). To support mechanism understanding and validate the experimental platform, finite element and multi-body dynamics simulations (ANSYS/ADAMS) are employed for physical verification and fault signature analysis, while the CNN-RF model is trained and tested exclusively using experimentally acquired vibration and acoustic data. For feature construction, vibration signals are transformed into time–frequency representations (including STFT, CWT, and generalized S-Transform (GST)), and acoustic signals are characterized using Mel-Frequency Cepstral Coefficients (MFCCs). Experimental results demonstrate that vibration–acoustic fusion improves diagnostic performance compared with single-modality baselines; the best performance is achieved by GST+MFCC with the proposed CNN-RF classifier, reaching an accuracy of 98.96%. Future work will conduct cross-condition validation under varying speeds and loads and investigate missing-modality robustness to further assess generalization and deployment reliability. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Advances in sensing technologies are increasingly transforming cutting operations by enabling data-driven condition monitoring, predictive maintenance, and process optimization. This review surveys recent developments in sensing modalities for cutting systems, including vibration sensors, acoustic emission sensors, optical and vision-based systems, eddy-current sensors, force sensors, and emerging hybrid/multi-modal sensing frameworks. Each sensing approach offers unique advantages in capturing mechanical, acoustic, geometric, or electromagnetic signatures related to tool wear, process instability, and fault development, while also showing modality-specific limitations such as noise sensitivity, environmental robustness, and integration complexity. Recent trends show a growing shift toward hybrid and multi-modal sensor fusion, where data from multiple sensors are combined using advanced data analytics and machine learning to improve diagnostic accuracy and reliability under changing cutting conditions. The review also discusses how artificial intelligence, Internet of Things connectivity, and edge computing enable scalable, real-time monitoring solutions, along with the challenges related to data needs, computational costs, and system integration. Future directions highlight the importance of robust fusion architectures, physics-informed and explainable models, digital twin integration, and cost-effective sensor deployment to accelerate adoption across various manufacturing environments. Overall, these advancements position advanced sensing and hybrid monitoring strategies as key drivers of intelligent, Industry 4.0-oriented cutting processes. Full article

To address the computational redundancy, inadequate multi-scale feature capture, and poor noise robustness of traditional deep networks used for bearing vibration and acoustic signal feature extraction, this paper proposes a fault diagnosis method based on Depthwise Separable Atrous Convolution (DSAC) and Acoustic Spatial Pyramid Pooling (ASPP). First, the Continuous Wavelet Transform (CWT) is applied to the vibration and acoustic signals to convert them into time–frequency representations. The vibration CWT is then fed into a multi-scale feature extraction module to obtain preliminary vibration features, whereas the acoustic CWT is processed by a Deep Residual Shrinkage Network (DRSN). The two feature streams are concatenated in a feature fusion module and subsequently fed into the DSAC and ASPP modules, which together expand the effective receptive field and aggregate multi-scale contextual information. Finally, global pooling followed by a classifier outputs the bearing fault category, enabling high-precision bearing fault identification. Experimental results show that, under both clean data and multiple low signal-to-noise ratio (SNR) noise conditions, the proposed DSAC-ASPP method achieves higher accuracy and lower variance than baselines such as ResNet, VGG, and MobileNet, while requiring fewer parameters and FLOPs and exhibiting superior robustness and deployability. Full article

This paper introduces an effective approach for rotatory fault diagnosis, specifically focusing on centrifugal pumps, by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and feature-level integration. Centrifugal pumps are critical in various industries, and their condition monitoring is essential for reliability. The proposed methodology addresses the limitations of traditional single-sensor fault diagnosis by fusing information from acoustic and vibration signals. CEEMDAN was employed to decompose raw signals into intrinsic mode functions (IMFs), mitigating noise and non-stationary characteristics. Weighted kurtosis was used to select significant IMFs, and a comprehensive set of time, frequency, and time–frequency domain features was extracted. Feature-level fusion integrated these features, and a support vector machine (SVM) classifier, optimized using the crayfish optimization algorithm (COA), identified different health conditions. The methodology was validated on a centrifugal pump with various impeller defects, achieving a classification accuracy of 95.0%. The results demonstrate the efficacy of the proposed approach in accurately diagnosing the state of centrifugal pumps. Full article

Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on one or more vibration sensors, and their diagnostic results are often unsatisfactory under strong noise interference. To tackle this problem, this research develops a bearing fault diagnosis technique utilizing a multi-channel, multi-scale spatiotemporal convolutional cross-attention fusion network. At first, continuous wavelet transform (CWT) is applied to convert the raw 1D acoustic and vibration signals of the dataset into 2D time–frequency images. These acoustic and vibration time–frequency images are then simultaneously fed into two parallel structures. After rough feature extraction using ResNet, deep feature extraction is performed using the Multi-Scale Temporal Convolutional Module (MTCM) and the Multi-Feature Extraction Block (MFE). Next, these traits are input into a dual cross-attention mechanism module (DCA), where fusion is achieved using attention interaction. The experimental findings validate the efficacy of the proposed method using tests and comparisons on two bearing datasets. The testing findings validate that the suggested method outperforms the existing advanced multi-sensor fusion diagnostic methods. Compared with other existing multi-sensor fusion diagnostic methods, the proposed method was proven to outperform the five existing methods (1DCNN-VAF, MFAN-VAF, 2MNET, MRSDF, and FAC-CNN). Full article

Wind turbines operate under harsh conditions, heightening the risk of rotating bearing failures. While fault diagnosis using acoustic or vibration signals is feasible, single-modal methods are highly vulnerable to environmental noise and system uncertainty, reducing diagnostic accuracy. Existing multi-modal approaches also struggle with noise interference and lack causal feature exploration, limiting fusion performance and generalization. To address these issues, this paper proposes CAVF-Net—a novel framework integrating bidirectional cross-attention (BCA) and causal inference (CI). It enhances Mel-Frequency Cepstral Coefficients (MFCCs) of acoustic and short-time Fourier transform (STFT) features of vibration via BCA and employs CI to derive adaptive fusion weights, effectively preserving causal relationships and achieving robust cross-modal integration. The fused features are classified for fault diagnosis under real-world conditions. Experiments show that CAVF-Net attains 99.2% accuracy with few iterations on clean data and maintains 95.42% accuracy in high-entropy multi-noise environments—outperforming single-model acoustic and vibration by 16.32% and 8.86%, respectively, while significantly reducing information uncertainty in downstream classification. Full article