Research on Fault Location Technology of Transformer Acoustic Feature Recognition and Field Perception Data Fusion under Small Sample Parameters

Research on Fault Feature Extraction and Intelligent Diagnosis Technology of Motor Drive in Embedded Systems

Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.

Cross-Attention Fusion of Time-Frequency Acoustic Features for Insulation Fault Recognition in Power Equipment Using a Microphone Array System.

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.

A Novel Acoustic-Vibration Fusion-Based Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers

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.

Data-Driven Noise Suppression and Multiresolution Feature Fusion with Feature Space Refinement for Bearing Fault Diagnosis

Multi-scale wavelet feature fusion with transformer for fault diagnosis of 25 Hz phase-sensitive track circuit

Aiming at the problems of long-range dependency modeling difficulty, weak fault feature extraction challenge, and severe class imbalance with limited minority fault samples in long time-series data of 25 Hz phase-sensitive track circuits, this paper proposes a fault diagnosis model called WT-Transformer by integrating discrete wavelet transform and Transformer encoder. Firstly, the original track circuit signal is decomposed by multi-scale discrete wavelet transform to extract global long-term trend features and local abrupt change features caused by faults. A soft-threshold denoising method based on the Minimax rule is adopted to suppress noise interference while retaining critical fault information. Secondly, the wavelet-enhanced signal and the original signal are combined by sample concatenation to enrich feature diversity and improve the identifiability of minority-class faults. Sine-cosine position encoding is integrated to provide temporal structure of the track circuit signal. Finally, a Transformer encoder with multi-head self-attention, feedforward network, and residual connection is constructed to capture long-range temporal dependencies and enhance feature representation ability. Experimental results on a simulated fault dataset show that the proposed model achieves superior performance in track circuit fault diagnosis, especially in the classification of rare faults with few samples. The effectiveness of wavelet transformation and time-frequency feature enhancement is verified, which provides a feasible and effective solution for intelligent fault diagnosis of long sequence data in track circuits.

Low-Cost Embedded System for Acoustic Fault Detection

In recent years, acoustic signal analysis has emerged as a promising approach for fault detection in electromechanical systems, providing a non-invasive, low-cost, and real-time alternative to traditional monitoring techniques. This paper presents the design and implementation of a low-cost embedded system for acoustic fault detection. The proposed system is built on a Raspberry Pi Nano platform, integrating a low-power microphone, real-time audio signal preprocessing, and lightweight machine learning models for classification of normal versus faulty operating conditions. The focus is placed on developing an efficient signal processing pipeline that includes noise reduction, feature extraction (time and frequency domain descriptors, Mel-frequency cepstral coefficients), and on-device classification using compact neural network architectures. The embedded setup enables autonomous monitoring without the need for external computation resources, making it suitable for edge deployment in industrial and IoT environments. Experimental validation is carried out using publicly available datasets such as MIMII (Malfunctioning Industrial Machine Investigation and Inspection), as well as preliminary real-time recordings. The results demonstrate that the system achieves reliable fault detection accuracy while maintaining low computational and energy costs, highlighting its potential for scalable deployment in smart maintenance applications.