Multi-classifiers ensemble with confidence diversity for fault diagnosis in induction motors

Motor is a kind of imperative driving device, whether a motor can monitor its state precisely and diagnose fault timely have a profound impact. This paper mainly investigates the improvement of the general method of motor defect diagnosis to achieve higher accuracy. Unfortunately, every classifier has their own respective advantages and disadvantages, using the typical machine learning methods separately cannot achieve the expectant classify results. So, fusing the result of multiple classifiers to fully exploit the advantages of each sensor to reach the requirement of improving the classification accuracy. In this paper, three types of classifiers are fused: naïve Bayes classifier, Random Forest classifier, and SVM classifier. By the algorithm of multi-classifier, the states of the motor can be predicted correctly.