Fault Diagnosis Method Based on MFCC Fusion and SVM

Abstract

In order to effectively improve the expressiveness and classification accuracy of fault signal characteristics of equipment, this paper proposed a fault diagnosis method based on Mel-Frequency Cepstrum Coefficient (MFCC) fusion and Support Vector Machines (SVM). First of all, the MFCC features, the Wavelet Packet Decomposition Energy features and the Zero-crossing rate (ZCR) features of the signal are separately extracted. Then, linearly combining the three features based on the MFCC features to obtain the MFCC fusion features. And the SVM classifier is used to classify the faults. Experiments show that compared with the traditional single MFCC features and Wavelet Packet Decomposition Energy features, the MFCC fusion features and SVM method have higher classification accuracy under the same noise environment.