Multi-model dual extended Kalman filtering for detection and isolation of current sensor and stator winding defects in a PMSM drive

A methodology for fault detection and isolation (FDI) of electrical faults in a three-phase permanent magnet synchronous motor (PMSM) is presented. The considered faults are stator winding and current sensor faults. The former consist of wire degradation (characterized by a rise in the corresponding phase resistance), turn-to-turn short circuits and phase-to-phase short circuits. The sensor faults consist of gain changes and offsets in phase current measurements. A multi-model adaptive estimator of fault parameters is developed by resorting to specific signal models. These models characterize phase current measurements (i.e. the signals) in the healthy and in each faulty operating mode. Such signal models do not require knowledge of the three-phase motor parameters. The severity of the fault is evaluated through the estimators that take the form of three dual extended Kalman filters (DEKFs) and one Kalman filter (KF). The proposed approach is validated by using synthetic data of phase current measurements. These data were generated by a digital twin of an electromechanical actuator (EMA) driven by a PMSM. This motor drive is subject to closed-loop speed control and includes realistic sensor noise. To test the robustness of the approach, its performance is evaluated with different levels of sensor noise.