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Mechanical Fault Diagnosis of Circuit Breaker Based on MFCC and Improved SRC

Circuit breaker plays an important part in power system. Mechanical faults are the main reason of the malfunction of circuit breaker. In order to diagnose the mechanical faults of circuit breaker, in this paper, the Mel Frequency Cepstrum Coefficients (MFCC) of the closing sound signals of circuit breaker are extracted based on the characteristics of human hearing. Then the Kernel Principal Component Analysis (KPCA) algorithm is applied to reduce the dimension of MFCC. The dimensionality reduction results of MFCC are taken as the feature vectors of closing sound signals. Sparse Representation Classification (SRC) algorithm is used to recognize the feature vectors. Meanwhile, the concept of scatter in Linear Discriminant Analysis (LDA) is applied into the objective function of SRC to improve the performance of SRC. Experimental results show that the method proposed in this paper can identify the circuit breaker mechanical faults accurately. MFCC could reflect the difference information of various mechanical states properly. Compared with SRC, the improved SRC algorithm that takes the with-class scatter and between-class scatter of the representation coefficients of feature vectors into account has better recognition performance. The identification process could provide theoretical guidance for field applications.