Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis

Abstract

This paper is focused on the design of a new approach dedicated to solve classification problems for the detection of broken rotor bar (BRB) fault in induction motors (IM). This new method finds its origins in a novel combination of both recursive undecimated wavelet packet transform (RUWPT) and directed acyclic graph support vector machines (DAG SVMs). Most often, BRB frequency components are hardly detected in the stator current due to its low magnitude and closeness to the supply frequency component. To overcome this drawback, the RUWPT is applied to extract one parameter able to detect the fault with arbitrary working conditions and a great concern of low load cases. Different multiclass support vector machines (MSVMs) methods are evaluated with respect to accuracy, number of support vectors, and testing time. The experimental results confirm that the DAG SVMs and Symlet wavelet kernel function are fast, robust, and give the best classification accuracy of 99%.