Fault diagnosis of motor bearing based on naive Bayes and weight analysis methods

LI Wan-qing
（Longmen Coal Company， Henan Coal Chemical Industry Group， Luoyang 471000，China）

Abstract： In order to analyze the types of motor bearing faults，wavelet package analysis was firstly used to decompose and reconstruct the signals of the ball bearings into different frequency bands，then thevalues of energies on each bands were used to compose feature vectors of ball bearings' signals. Those vectors，which were considered as samples，were used in naive Bayes and weight analysis models respectively to complete the classification of motor bearing faults. Naive Bayes network trained the traing samples （whose type are known），and then classified the testing samples（whose type are unknown）. In weight analysis model，the Euclidean distances of each testing samples and training samples were computed，and the types of all testing samples were obtained through the constructed weights. The simulation results show that，through wavelet package analysis，Naive Bayes can deal with motor bearing fault well，and the weight analysis method can also analyze the fault signals effectivly.
Key words： fault of motor； wavelet package； Naive Bayes； weight analysis