SVM based diagnosis of inverter fed induction machine drive: A new challenge

In fault diagnosis studies two main approaches are mostly used. The first one consists in designing the full physical or empirical model of the system in healthy and faulty conditions. The major drawback of this approach is the difficulty to obtain an accurate model reflecting all the operating conditions and phenomena. The second approach, used in this work, consists in using signal processing techniques for the characterization of the healthy and faulty behaviors. This paper deals with the study of a fault detection and isolation procedure on a three phase inverter feeding an induction machine drive using pattern recognition techniques. The diagnosis procedure relies on the use of classifiers after the collection of the output currents of the inverter flowing in the machine windings. The proposed classifiers are based on Support Vector Machines (SVM). We show in this paper how it is possible to tune the SVM and also the influence of the data normalisation to perform an effective diagnosis with experimental data.