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

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.