Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis

In the acoustics-based power transformer fault diagnosis, a transformer acoustic signal collected by an acoustic sensor is generally mixed with a large number of interference signals. In order to separate transformer acoustic signals from mixed acoustic signals obtained by a small number of sensors, a blind source separation (BSS) method of transformer acoustic signal based on sparse component analysis (SCA) is proposed in this paper. Firstly, the mixed acoustic signals are transformed from time domain to time-frequency (TF) domain, and single source points (SSPs) in the TF plane are extracted by identifying the phase angle differences of the TF points. Then, the mixing matrix is estimated by clustering SSPs with a density clustering algorithm. Finally, the transformer acoustic signal is separated from the mixed acoustic signals based on the compressed sensing theory. The results of the simulation and experiment show that the proposed method can separate the transformer acoustic signal from the mixed acoustic signals in the case of underdetermination. Compared with the existing denoising methods of the transformer acoustic signal, the denoising results of the proposed method have less error and distortion. It will provide important data support for the acoustics-based power transformer fault diagnosis.

Automated summary

This paper proposes a blind source separation method based on sparse component analysis for handling the problem of interference signals in acoustics-based power transformer fault diagnosis. The method transforms the mixed acoustic signals into the time-frequency domain, extracts single source points, estimates the mixing matrix through clustering, and separates the transformer acoustic signal from the mixed acoustic signals using compressed sensing theory. The simulation and experimental results demonstrate that the proposed method successfully separates the transformer acoustic signal even underdetermination.