Motor Fault Diagnosis Based on Decision Tree-Bayesian Network Model

Motor is widely used in various industries, at the same time, it leads to motor fault diagnosis along with the rapid development of technology. Traditional motor fault diagnosis methods have not quickly and accurately diagnose the motor faults. Therefore, by analyzing the characteristics of the decision tree and the advantages of Bayesian network in dealing with uncertain information, which advances to use the decision tree combining with Bayesian network to diagnose motor fault, so that the diagnosis can be more accurately and quickly. This paper also describes the model structure and the basic ideas of decision tree and Bayesian network, combines the advantages of the two, and solves the uncertainty of diagnosis information effectively. It has practical significance.