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Research of bearing fault diagnosis method based on wavelet singular entropy and support vector machine

Electric Motors play a vital role in various aspects of production and life. Once a motor fails, the loss will be incalculable, and the state of motor bearings will directly affect the reliability of motor operation. Due to the high failure rate of motor bearings and the difficulty of accurately identifying fault types, a fault diagnosis method based on Wavelet Singular Entropy (SEM) and Support Vector Machine (SVM) was proposed. The fault was implanted into the motor bearing by electro-discharge machining (EDM), and the vibration signal samples of 4 different state types were measured. The characteristics of the four state type vibration signals are extracted with wallet singular entropy, and the state classification of the data after feature extraction is carried out by the support vector machine classifier. The experimental research shows that the fault diagnosis method of motor bearing based on wavelet singular entropy and support vector machine can greatly improve the fault diagnosis accuracy.