Induction Machine Fault Diagnosis Using Stator Current Subspace Spectral Estimation

This paper presents a subspace-based approach to identify and extract harmonics of interest for the diagnosis of stator and rotor related faults in induction machines. The major goal of this paper is firstly to introduce and highlight the effectiveness of prominence measure upon preparing features for classification of faults. Secondly, a new approach is presented here to retrieve harmonics by using prominence measure of the peaks for each case of the fault. Finally, a hierarchical multi-layer perceptron neural network has been used as the classifier and compared with other existing classification algorithms to deduce the best model. The effectiveness of the developed scheme is verified experimentally.