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. In this paper, a diagnosis method based on statistical pattern recognition (PR) analysis associated with a new state estimator is used to detect and to classify automatically the electrical faults, shortand open-circuit under any level of load of the studied system: redundant three-phase power converter fed 6/4 SRM. The phases making a PR analysis, the training and the decision phases have been developed in the paper. The training data is carried out using a set of fault scenarios, between healthy, single and combined faults, in terms of torque at different load level, 25%, 50% and 75% of the nominal load in order to deduce the fault severity. Each case is then analyzed numerically. The training set being not exhaustive, it is impossible to have measurements with and without fault, for any the level of load. For this purpose, Kalman estimator is used to tracking of various operating modes and to predict the evolution of the call out of the knowledge database (for instance for overload or the more sever fault) for a given operating mode in order to realize a preventive maintenance. Kalman filter has two steps: the prediction step, where the next state of the system is predicted given the previous measurements, and the update step, where the current state of the system is estimated given the measurement at that time step. The results show the superiority of Kalman predictor in the estimation and prediction changes in the severity of the SRM failure as it has been demonstrated that pattern recognition methods can be successfully applied to the diagnosis of switched reluctance motors fed by a power converter.