This paper presents the theory, simulation, diagnosis and prognosis evaluation of an anomaly detector for Permanent Magnet Synchronous Motor (PMSM) stator winding insulation faults. Physics-of-failure mechanisms are used to develop the PMSM model and its insulation fault model. Then, the diagnostic features are identified using Hilbert transforms based on artificial data acquired from the simulation results of different degree of the stator winding insulation faults. Next, the diagnosis and prognosis routine pass the diagnostic features to the Extended Kalman Filter (EKF) based on Bayesian estimation theory. Finally, the real-time diagnosis and prognosis of an anomaly detector for PMSM stator winding insulation faults are performed using Simulink. Simulation results are presented to demonstrate the effectiveness of the proposed method.
How to Cite
Diagnosis and Prognosis, PMSM, insulation faults, EKF, Diagnostic features, Hilbert transforms
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