Fault detection and diagnosis in a set "inverter-switched reluctance motor" based on pattern recognition using Kalman filter prediction

In the case of one phase failure, the switched reluctance motor (SRM) will behave nearly the same, both in open circuit and in short circuit failure. This means, that the machine will understand the two faults in the same way which makes the SRM faults detection and diagnosis a more challenging task. This paper presents a diagnosis method based on pattern recognition analysis to detect and to classify automatically the electrical faults, short-and open-circuit under any level of load of the studied system: redundant three-phase power converter fed 6/4 SRM. The phases making a pattern recognition diagnosis of SRM, the training and the decision. The training phase consists in determining the pattern vector and the optimal kernels design (the separating classes) by Time-Frequency Representation (TFR). The training data is carried out using a set of fault scenarios, between healthy, single and combined faults, in terms of torque measurement at different load level, in order to deduce the fault severity. The second phase, consists in associating an unknown pattern with one of the defined classes, according to the "k-nearest neighbors" (knn) decision rule, associated with Kalman estimator to tracking of various operating modes and to predict the evolution of the call out of the knowledge database for a given operating mode in order to realize a preventive maintenance. The experimental results prove the efficiency of pattern recognition methods in condition monitoring of reluctance machine.