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Induction motor fault diagnosis based on ensemble classifiers

With increasing demand for accurate fault diagnosis of induction motors, traditional methods based on single parameter need amelioration. In this paper, an effective and practical induction motor fault diagnosis algorithm is proposed based on adaptive weighted voting multiple random forest classifiers. Firstly, the vibration signals and stator current signals are obtained and analyzed. The energy features at several characteristic frequencies related to motor faults from each type of signal are extracted and used as input to corresponding random forest classifier. Then clustering analysis is applied to both testing and training samples to determine the weight of each classifier for decision making on diagnostic result. Experimental study performed on induction motor data has verified that the classifier fusion algorithm can improve the diagnostic accuracy.