Model-Based Diagnosis and RUL Estimation of Induction Machines Under Interturn Fault

In this paper, a model-based integrated framework for diagnosis and prognosis of electrical machines is proposed. Since the fault progression, compared to the dynamics of the measured terminal quantities, can be considered as a slow process, it can be approximately modeled by the step changes of certain model-based parameters, representing fault severity levels. Diagnosis process can reliably detect the fault occurrence and severity changes, from the incipient stage of fault development, by which the parameter estimation and prognosis process, leading to calculation of remaining useful life, can be promptly commenced. A generic method using multiple fault indicators for diagnosis and particle filter for prognosis is proposed for electrical machines under stator winding interturn fault. The proposed framework is experimentally validated using real machine signatures consisting of only stator voltage and current measurements.