Fault Identification in the Stator Winding of Induction Motors Using PCA with Artificial Neural Networks

Three-phase induction motors are the main element of electrical into mechanical energy conversion applied in the industries. Due to its constant usage, added to adversities such as thermal, electrical and mechanical, these motors can be damaged causing unexpected process losses. Among the drawbacks of occurrences commonly presented for this equipment, approximately 37 % are related to short circuit in the stator coils. Hence, this article proposes an alternative approach for stator fault identification in induction motors through the discretization of the current signal, in the time domain, applying a variable optimization technique of principal components analysis (PCA) and artificial neural networks (ANNs) types multilayer perceptron (MLP) and radial basis function. Experimental results are presented with data gathered from an experimental workbench, considering various supply conditions and also under a wide load variation, by using the amplitude of the current signals in the time domain. Moreover, the MLP network presented the best results and the PCA technique provided a considerable reduction in the number of ANNs inputs, and in general, the classification results were comparable to the results obtained when the networks inputs considered the technique employing downsampling of 30 points to represent the current signals using half-cycle of the waveform.