Jul 2026· Journal of Visualized Experiments· Vol 233· 0 citations
Medicine
TL;DR
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.
Abstract
The goal of this protocol is to provide a non-contact method for recognizing insulation faults in power equipment by extracting and fusing time- and frequency-domain acoustic features for automated fault classification. Acoustic fingerprints generated by insulation faults exhibit nonlinear and non-Gaussian characteristics, which can limit the effectiveness of conventional single-domain feature extraction approaches. To address this challenge, this protocol presents a time-frequency feature extraction and fusion framework based on a cross-attention (CA) mechanism for the identification of four insulation fault types. Time-domain features are first extracted using a temporal convolutional network coupled with an autoencoder to capture dynamic variations in sequential acoustic signals. In parallel, frequency-domain features are extracted from Mel spectrograms using a convolutional block attention module to enhance spectral feature representation. A CA mechanism is then employed to adaptively fuse temporal and spectral features, strengthening the relationships between the two feature domains. The fused feature representation is subsequently input into a one-dimensional convolutional neural network optimized using the Cuckoo Search algorithm for fault classification. Representative results demonstrate that this framework effectively characterizes complex acoustic fingerprints and achieves high classification performance relative to conventional feature extraction approaches. The protocol provides a robust non-contact strategy for insulation fault diagnosis and condition monitoring of electrical power equipment.
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
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.
Kai Zhang, Hongming Lu, Jinning Chen et al.· IEEE Sensors Journal· 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
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
Acoustic emission (AE) enables real-time structural health monitoring with high sensitivity. However, overlapping signals from multiple concurrent sources—known as mixed-mode AE—pose major challenges for accurate damage classification. This paper presents a novel identification approach utilizing a deep learning-based ensemble method combined with tailored pre- and post-processing techniques. By segmenting time–frequency spectrograms of AE hits into frequency bands, the convolutional neural networks ensemble effectively extracts features to distinguish constituent damage modes within mixed signals. Among several architectures evaluated, DenseNet achieved the highest classification accuracy, exceeding 98.86% on independent test data. Information entropy analysis further confirmed clear spectral distinctions between pure-mode and mixed-mode AE signals, consistent with theoretical predictions. Model interpretability analysis elucidated the basis for model decisions and directions for improvement. Leveraging the reliable predictions, the coupling relationships between damage modes were deduced, and the finite element method was introduced to further explain the physical essence of coupling transformation, revealing the decisive role of the out-of-plane peeling stress in adhesive debonding and fiber breakage. Additionally, Gaussian process regression (GPR) models verified the existence of the mixed-mode signal formation pattern. Overall, the proposed method offers a robust solution for analyzing complex AE signals and provides new insights into the intrinsic mechanisms of AE activity in composite structures.
This study presents an acoustic camera-based approach for characterizing partial discharge (PD) signals in a 20 kV switchgear using time-frequency analysis. Acoustic signals were acquired using an acoustic camera and processed through audio extraction, bandpass filtering, and segmentation. Subsequently, Short-Time Fourier Transform (STFT) and Melspectrogram representations were employed to analyze the time-frequency characteristics of the recorded signals. Several features, including Root Mean Square (RMS), Spectral Centroid, Band Energy Ratio (BER), Entropy, Mel Energy, and Mel Entropy, were extracted to characterize the energy and frequency distributions associated with different PD conditions. Experimental measurements were conducted under five operating conditions, namely normal, corona, void, surface, and arc discharges. The results reveal distinct spectral patterns and feature distributions for each discharge type, demonstrating the capability of time-frequency analysis to capture characteristic acoustic signatures of PD activity. The proposed approach provides a systematic framework for acoustic PD characterization and contributes to a better understanding of discharge-related acoustic behavior in medium-voltage switchgear applications.
Gregorius Satrio Kuncoro, Daniar Fahmi, Hendra Kusuma· International Seminar on Int...· 0 citations