Research on Motor Bearing Fault Diagnosis Based on the AdaBoost Algorithm and the Ensemble Learning with Bayesian Optimization in the Industrial Internet of Things

Abstract

In order to adapt to the development of the industrial Internet of Things, the relationship between the internal components of electromechanical equipment is getting closer and closer, such as motor bearings. Nowadays, timely diagnosis of motor bearing faults is urgently needed. Most traditional methods for motor bearing fault diagnosis use a single learner and emphasize the role of feature extraction, which usually requires a large amount of sample support and computer runtime to obtain satisfactory performance. In this article, the Bayesian optimized decision tree with ensemble classifiers after feature extraction of the original data is finally proposed which has good performance. We use multiple feature extraction to establish the feature matrix and construct a decision tree model with the ensemble method for AdaBoost and a Bayesian optimized decision tree model with ensemble classifiers to conduct experiments on the accuracy, prediction speed, etc., of the model. We derived four sets of experimental data. The results show that the optimal method is the Bayesian optimized decision tree with ensemble classifiers after feature extraction. The accuracy of this method is as high as 99.9%. At the same time, unlike previous studies, we found in our study that feature extraction does not improve the accuracy of diagnosis for the decision tree with ensemble method for AdaBoost and there is a precipitous decline. In the industrial Internet of Things, the conclusion can improve certain reference value for the future fault diagnosis of motor bearings.