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

Summary

This protocol describes 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.

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.

Introduction

Early identification of insulation faults in power equipment is critical for ensuring the safety and stability of the power grid^1,2,3,4. Statistics indicate that approximately 60% of power equipment failures are directly associated with insulation degradation⁵. Conventional electrical monitoring methods, however, often suffer from delayed response, poor anti-interference capabilities, and the requirement for invasive, contact-based installation⁵. Recently, acoustic fingerprint recognition has e....

Protocol

This study did not involve human participants, vertebrate animals, or biological specimens. Therefore, institutional ethics approval, animal-use approval, and human-subject approval were not required.

Figure 1 illustrates the dilated causal convolution structure used within the TCN architecture. Figure 2 illustrates the residual connection structure used during temporal feature extraction. Figure 3 illustrates the AE architecture used for feature-dimension reduction. Figure 4 illustrates the CBAM architecture used for spec....

Results

Time- and Frequency-Domain Feature Extraction Framework

The proposed insulation-fault identification method integrates temporal and spectral feature extraction pathways to characterize the complex acoustic signatures generated by insulation defects. Time-domain feature extraction was performed using a TCN incorporating dilated causal convolutions to capture long-range temporal dependencies within the acoustic sequences (Figure 1). Residual connect.......

Discussion

This method article delineates a standardized, non-contact protocol for identifying complex insulation faults in electrical power equipment through acoustic fingerprint recognition. By integrating a TCN-AE alongside a Mel spectrogram processed by a Mel-CBAM, the established pipeline isolates both dynamic time-series fluctuations and localized spectral characteristics. The subsequent cross-modal fusion via a CA mechanism is intended to reduce the information dilution that often accompanies simple feature concatenation. Op.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Autoencoder (AE)	Custom model	Encoder-decoder architecture for feature compression	Feature dimensionality reduction
Cross-Attention module	Custom model	Time-frequency feature fusion module	Cross-modal feature fusion
GCC-PHAT algorithm	Custom algorithm	FFT/IFFT-based implementation	Time-delay estimation and signal alignment
High-voltage test circuit	Self-built laboratory platform	Autotransformer T1, test transformer T2, 200 kΩ protective resistor, 1 nF coupling capacitor; operating voltage 4–12 kV	Generation of insulation-fault conditions
Mel spectrogram + CBAM	Custom model	Mel-frequency spectral extraction with attention enhancement	Frequency-domain feature extraction
Microphone array system	Self-built laboratory platform	112-channel modified annular spiral array; 200 kHz sampling rate; 8192 points per frame	Acoustic acquisition
Microphone sensors	Self-built matched ultrasonic microphone modules	112 matched channels; sensitivity -38 +/- 3 dBV/Pa; 20 Hz-100 kHz frequency response; calibrated at 94 dB SPL/1 kHz and 40 kHz; acceptance limits: +/-2 dB at 1 kHz and +/-3 dB at 40 kHz	Acoustic detection
Multichannel DAQ system	Self-built synchronized DAQ platform	112-channel simultaneous acquisition; 16-bit ADC; 200 kHz/channel; 8192 points/frame; shared sampling clock; positive-edge TTL hardware trigger	Data acquisition and synchronization
One-dimensional CNN + CS optimization	Custom model	Five convolutional layers; kernel size 3; SGD; learning rate 0.001; batch size 50	Fault classification
Python training environment	Python / PyTorch	Python 3.9; PyTorch 2.0; NumPy 1.24; scikit-learn 1.2; librosa 0.10; CUDA 11.8	Model training and evaluation
Temporal Convolutional Network (TCN)	Custom model	Dilated causal convolutions with residual connections	Time-domain feature extraction
Test cabinet with defect models	Self-built laboratory platform	Surface discharge, suspended discharge, internal discharge, and point discharge models	Simulation of insulation faults
Transient Earth Voltage (TEV) testing system	Commercial TEV monitoring module	TEV reference channel; 3-100 MHz analog bandwidth; 40 dB gain; positive-edge TTL trigger; trigger threshold set 6 dB above background noise floor or >=10 mV equivalent at sensor input	Verification of discharge activity