Author(s)

Jinshan Lin

Corresponding Author

Jinshan Lin

ABSTRACT

In view of the large noise of the collected signals in the current fault diagnosis methods of motor bearings, the incomplete feature extraction, and the low efficiency and accuracy of fault diagnosis, this paper proposes a method based on multi-domain signal feature extraction and Bayesian Optimized decision tree fault diagnosis method for motor bearings. The method mainly includes signal feature extraction and optimized decision tree fault diagnosis. First, the bearing vibration signal data set is used to extract feature values in the time domain, frequency domain, and time-frequency domain respectively; then Bayesian optimization is used to supervise the decision tree. The parameters are optimized, and the training data is used to train and predict the optimized decision tree classifier. At the same time, the same data set is used to train the decision tree model without feature value extraction, the optimized decision tree model without feature value extraction, and the decision tree model with feature value extraction. The diagnosis results of the four models are compared, and the results show that the method can effectively reduce the dimensionality of the signal data, and can greatly improve the fault diagnosis rate and the fault diagnosis accuracy rate.

KEYWORDS

Fault diagnosis Multi-domain feature extraction Decision tree Bayesian optimization CART