Fault detection in electric motors by means of the extended Kalman Filter as disturbance estimator

In this paper, an approach for disturbance estimation in the stator phase currents of an induction machine is presented. The approach is based on the Extended Kalman Filter that uses the extended model of an electrical induction machine (IM) under healthy conditions. The extended model includes additional states (disturbances) that allow discrepancies between the model and the real system to be detected. It is demonstrated through simulation that the method is able to identify anomalies when unmodelled dynamics are induced. Subsequently, the values of the estimated disturbances may be used as inputs to a condition monitoring system in order to detect machine faults, helping to reduce the rate of spurious stops and false alarms, therefore improving the overall process efficiency. Additionally, the disturbances could be taken into account in the control system of the motor improving the machine performance.