Aug 2026· IEEE Sensors Journal· Vol 26, pp. 25309-25320· 0 citations· 28 references
Abstract
Acoustic-vibration multimodal fusion technology has developed rapidly in the field of equipment fault diagnosis due to its ability to effectively suppress noise interference and enhance diagnostic reliability. However, existing acoustic-vibration multimodal fusion methods have not been adapted to the transient impact characteristics and structural features of circuit breakers, and still suffer from issues such as modal heterogeneity, feature redundancy, poor noise robustness, and insufficient real-time performance. To address these challenges, this article proposes an acoustic-vibration multimodal fusion and shared-private decoupled network method for high-voltage circuit breakers. For vibration signals, a wavelet synchronous compression transform (WSST) is employed to enhance time-frequency resolution and accurately capture transient impact characteristics. For acoustic signals, per-channel energy normalization-Mel spectrum is constructed to suppress steady-state background noise and enhance transient fault features. Based on the modal decoupling theory, a shared-private dual-branch encoder is designed to decouple cross-modal-shared information from single-modal unique features, and combined with the self-attention mechanism to achieve deep fusion of multimodal features. The experimental results show that the proposed method achieved a diagnostic accuracy of 98.02% on the test set. Compared with existing diagnostic methods, this method offers higher diagnostic accuracy, greater robustness, and better engineering practicality, providing a viable technical solution for intelligent online fault diagnosis of high-voltage circuit breakers.
To address the limitation that a single sensor is insufficient for comprehensively extracting deep fault features in strong industrial noise environments, which constrains bearing diagnosis accuracy, this paper proposes an acoustic-vibration collaborative fusion network. First, an Adaptive Gated Residual Block (AGRB) is designed and combined with a Twin-Gated Residual Block (TGRB) architecture to effectively extract highly robust deep local acoustic and vibration features amidst strong background noise. Second, a Bidirectional Attention Sensing Module (BASM) is constructed to perform deep interaction and complementary calibration of heterogeneous acoustic-vibration features in the global semantic dimension, breaking through the limitations of traditional shallow concatenation of multimodal features. To verify the effectiveness of the proposed model, an experimental study was conducted on a 6205 deep groove ball bearing using a non-contact acoustic-vibration synchronous acquisition system with a 25 cm acoustic monitoring distance and a 5096 Hz sampling rate. The dataset contains nine diagnostic categories, including one healthy state and eight fault states.Experimental results indicate that this method can achieve deep dynamic alignment of heterogeneous data. The VA-DFN demonstrates exceptional noise-resistant robustness under varying signal-to-noise ratio (SNR) conditions from −6 dB to 2 dB, achieving a maximum diagnostic accuracy of 99.55%, which is significantly superior to existing single-modality and conventional deep learning baseline models.
Fanlong Zhu, Junyu Lai, Peiwen Lu et al.· Measurement and control (Lon...· 0 citations
In engineering applications, mechanical equipment must adapt to complex and dynamic working environments, where the rotational speed often varies over time, resulting in significant distribution discrepancies across different operating conditions. Meanwhile, information obtained from a single vibration signal is often insufficient and susceptible to external interference. Traditional single-source domain adaptation methods may suffer from negative transfer and fail to effectively exploit complementary knowledge from multiple source domains for target-domain fault diagnosis, resulting in reduced reliability and generalization performance of diagnostic models. To address these limitations, this paper proposes a Progressive Multi-Dimensional Multi-Source Domain Adaptation (PMMDA) method. From the perspective of collaborative utilization of multi-source data, the proposed method integrates multimodal information from vibration and acoustic signals and employs a multi-level feature alignment strategy to achieve progressive alignment between source and target domains. Additionally, an adaptive weighting mechanism is introduced to dynamically balance the contributions of different source domains during model training, thereby enhancing the overall learning performance. Experimental results on two sets of bearing fault diagnosis tasks under time-varying rotational speed conditions demonstrate that the proposed method can effectively mitigate the impact of distribution discrepancies, significantly improving the accuracy and generalization capability of the diagnostic model, and verifying its potential and reliability in complex operating conditions.
He Qin, Zhongwei Zhang, Xinyu Li et al.· Proceedings of the Instituti...· 0 citations
This protocol provides a robust non-contact strategy for insulation fault diagnosis and condition monitoring of electrical power equipment by extracting and fusing time- and frequency-domain acoustic features for automated fault classification.
Weifeng Chen, Chunguang Hou, Yu Gu et al.· Journal of Visualized Experi...· 0 citations
To address the problems that weak impulsive features in acoustic–vibration signals are easily masked under strong noise, fault severities within the same fault category are difficult to distinguish, and existing fusion models show insufficient coordination between local details and global semantics, this paper proposes an acoustic–vibration fusion method for bearing fault diagnosis based on a multi-scale Swin–CNN hybrid architecture. The proposed method first employs a Bayesian optimization-based tunable Q-factor wavelet transform (BO-TQWT) to enhance fault-sensitive subbands under low signal-to-noise ratio conditions, and then converts acoustic and vibration signals into two-dimensional time–frequency maps. Subsequently, a Swin–CNN hybrid network is constructed, in which CNNSwinBridge performs a two-stage, single-pass cross-guided recalibration between CNN-derived local features and Swin-derived contextual features. Specifically, Swin features provide channel-wise semantic guidance for CNN features, whereas the recalibrated CNN features provide spatial texture guidance for Swin features. The module provides lightweight cross-architecture coordination without recurrent or iterative feedback. Experimental results on the BJTU-RAO and University of Ottawa bearing datasets show that the proposed method achieves average accuracies of 99.51% and 99.96%, respectively, under clean operating conditions. Further analysis indicates that BO-TQWT provides relatively limited performance gains under clean conditions, whereas it can more effectively enhance fault-sensitive features under strong noise, thereby improving the fine-grained discrimination of different severity levels within the same fault category.
Mengran Liu, Zhao-Tao Du, Zhen-Xiang Xiong et al.· Measurement science and tech...· 0 citations
Reliable transformer fault diagnosis under limited fault samples remains a significant challenge in intelligent power systems. To address the difficulties associated with weak fault signatures, severe environmental interference, and insufficient training samples, this study investigates transformer fault location technology based on acoustic feature recognition and field perception data fusion. The generation mechanism and propagation characteristics of transformer acoustic signals are first analyzed, and an improved time–frequency feature extraction method is developed to enhance feature representation under small-sample conditions. A multi-physics data fusion framework integrating acoustic, vibration, and electrical sensing information is then established, and a dedicated attention mechanism is designed to achieve deep feature fusion across heterogeneous data sources. Finally, an enhanced deep neural network model is employed for accurate fault localization and condition identification. Experimental results demonstrate that the proposed framework effectively improves fault recognition performance and location accuracy under small-sample constraints. The study provides technical support for intelligent power equipment monitoring and offers methodological references for signal propagation analysis, sensor fusion, and electromagnetic condition monitoring systems.
Y. G. Li, L. J. Feng, R. R. Li et al.· Advanced Electromagnetics· 0 citations
Existing intelligent fault diagnosis methods based on multimodal fusion face the problem of significant differences in the representation capabilities of different modal data for machine faults, making it difficult to achieve optimal cross-modal data fusion and accurate fault identification. This study proposes a prior-enhanced cross-modal vibration and acoustic data fusion network based on directed attention mechanisms to address the aforementioned issue.First, through preliminary experiments in fault diagnosis, the differences in fault sensitivity between vibration and acoustic data are measured to determine the prior dominant data modality. Then, based on the directed cross-attention mechanism, a prior-dominant modality-weighted fusion of vibration and acoustic data features is realized. This process allows for unidirectional feature information transfer from the dominant data modality to the weaker one, avoiding reverse information contamination. Thus, cross-modal fusion features that are more sensitive to machine faults can be extracted. Finally, the extracted cross-modal fusion features are used to achieve fault diagnosis. The results of two machine fault experiments demonstrate that, compared with the state-of-the-art methods, the proposed method can achieve a significant leading advantage in the same diagnostic tasks, with a identification accuracy rate of bearing faults reaching 0.9898 under noisy conditions.
Qi-Bo Wang, Tianci Zhang· Measurement science and tech...· 0 citations
