In view of such issues as relying too much on subjective experience

low accuracy and weak generalization ability of fault diagnosis method of traditional 10 kV high-voltage circuit breaker

a kind of common mechanical fault identification method for 10 kV high-voltage circuit breaker based on acoustic features is proposed. Firstly

type (ZN63) 12/630A high-voltage indoor vacuum circuit breaker is taken as the research object

8 kinds of common mechanical faults are set and the sound during opening and closing operation is collected as the detection signal. Then

the collected fault voiceprint signals is preprocessed

the Mel cepstrum coefficient (MFCC) features

chromat features

one-dimensional average energy

and spectral centroid of the fault voiceprint signals are extracted

and Fisher ratio to discard components with lower contribution rates is used to form fusion features. Finally

the extracted fusion features is taken as the diagnostic basis

a residual neural network based mechanical fault diagnosis model for 10 kV circuit breaker is constructed. The results show that the accuracy for diagnosing and identifying 8 common mechanical faults of 10 kV high-voltage circuit breaker by the method proposed in this paper is 99.99%. It can serve as an effective supplement to the current detection methods

improving the comprehensive detection and latent defect identification capabilities of high-voltage circuit breaker.