In view of such issues as complicated mechanical fault and difficult fault extraction of high voltage circuit breaker

a kind of joint acoustic and vibration fault diagnosis method for circuit breaker based on the combination of VMD and KFCM-SVM is proposed. Firstly

VMD is used to decompose the denoised vibration and acoustical signals

and a series of intrinsic mode functions (IMF) reflecting the mechanical state information during operation of the circuit breaker are obtained. Then

Hilbert transform is performed on each IMF component to construct corresponding Hilbert marginal spectrum

and the energy entropy is obtained as the feature vector. Finally

kernel fuzzy C means (KFCM) is used to presort the features

and then the support vector machine (SVM) is used to set up a training model to achieve mechanical state identification. The experimental results show that the energy entropy of the Hilbert marginal spectrum of the acoustic vibration signal is sensitive to the mechanical state variation of high-voltage circuit breaker

and KFCM-SVM can accurately identify mechanical faults during the opening operation of high-voltage circuit breaker.