Fault diagnosis method of high voltage circuit breaker based on the combination of time-frequency multi-characteristics of acoustic signal

Aiming at the problem of accurately identifying the mechanical state of circuit breaker in the actual operation environment, a new fault diagnosis method of high voltage circuit breaker based on the combination of time-frequency multi-characteristics of acoustic signal was proposed. Firstly, the background noise database was established to remove the template noise. On this basis, the adaptive wavelet transform (AWT) was used to remove the residual noise. Then the kurtosis, crest factor and skewness indexes were extracted respectively to construct the time-domain characteristics. The acoustic signal was decomposed by variational mode decomposition (VMD) to obtain the IMF component. The power spectrum of the IMF was converted to the polar coordinates of the divided sub-region. The sensitivity of the main peak region was improved by the divergence factor, and the spectral difference entropy characteristics were calculated. The two jointly constructed the time-frequency multi-characteristics. Finally, kernel fuzzy c means (KFCM) clustering was used to pre-classify the characteristics, and then support vector machine (SVM) was used to establish training models to realize mechanical state identification. The diagnosis result shows that the accuracy of time-frequency multi-characteristics combined with KFCM-SVM diagnosis method is 98.75 %. It can reflect the status information of circuit breaker from multiple dimensions, and has high practical popularization value.