Fault diagnosis of induction motor based on a novel intelligent framework and transient current signals

This paper deals with fault diagnosis of induction motor containing common faults by using a novel intelligent framework and transient stator current signals. This framework consists of a Fourier-Bessel (FB) expansion for analyzing the transient signals, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. The start-up transient current signals are acquired from different motor operating conditions and decomposed into single components using FB expansion. Subsequently, a number of statistical features in the time domain and the frequency domain are computed for each component to represent the motor conditions. The high dimensionality of the feature set is reduced by implementing GDA. Finally, the diagnosis performance is carried out by RVM, which is an intelligent method in pattern recognition area. The framework has been applied for traction motor faults including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance, and phase unbalance in general applications. The results show that the proposed diagnosis framework is capable of improving the classification accuracy significantly in comparison other methods.