Model-based failure prediction for electric machines using particle filter

摘要

With the increasing demand of high reliability and safety of modern electric machines, failure prognosis becomes more and more important since it is efficient to increase reliability and reduce downtime cost. In this work, a model-based remaining useful life (RUL) prediction method is developed for induction motor with stator winding short circuit fault. The induction motor model with stator winding short circuit fault is introduced based on reference frame transformation theory. The winding short circuit fault is characterized by the fraction of short turns and the fault loop resistance. In this paper, the motor life is defined as the stator winding insulation life due to thermal stresses because from a thermal point of view, the stator winding insulation is the weakest part of induction motors. A particle filter method is used to realize unknown parameter estimation and RUL prediction. Simulation results are provided to validate the proposed method.