A neural approach for the fault diagnostics in induction machines

The motor current signature analysis has been considered as a standard for both electrical and mechanical fault detection in three-phase induction machine. However, even if the spectrum analysis is well known and has been published intensively in the literature, the problem of automatic classification for fault detection is still open. The aim of this paper is to present a new neural network approach for fault and speed detection using the current spectrum analysis. After the presentation of an original database creation, the classification using a particular neural network is developed. This network is conceived in order to output the posterior probabilities of class membership of the input, which allow the estimation of the level of confidence of the classification. Then, the classical motor slip detection algorithm is verified and the classification is experimentally performed on a squirrel-cage three-phase induction machine. The efficiency of the iteration process is shown together with the confusion matrix for the current spectrum analysis in the proposed method.