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Fault Diagnosis Method of Motor Bearing Based on Information Entropy and Multi-scale Wavelet Entropy

The bearing condition of electrical equipment has a great impact on the reliability of the power system operation. The operating conditions of electrical equipment are complex and variable, and the vibration signal of its bearings is unstable and non-linear. The traditional time-domain characteristics are not longitudinally comparable, and it is impossible to discern whether the vibration changes come from the inner ring, outer ring or rolling body of the bearing when only using the time-domain characteristics. To solve this problem, a method of extracting characteristic parameters of vibration signal by multi-scale wavelet entropy and information entropy is further introduced in this paper. The multi-scale wavelet entropy and information entropy of bearing vibration signals in different states are used as the training set of the model. And the fault state recognition model of the electrical equipment is established by training the support vector machine (SVM) model. After wavelet entropy and information entropy feature extraction, the training time is considerably shortened, and the diagnosis accuracy is significantly improved, which shows that the method is effective and practical for the fault diagnosis of electrical equipment and provides a new idea for fault diagnosis of electrical equipment.