Inter-Turn Short Circuit Fault Detection and Diagnosis in Permanent Magnet Synchronous Motors Using Interactive Multiple Model Strategy

This paper introduces a novel approach for diagnosing Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) using a two-layer Interactive Multiple Model (IMM) strategy tailored for real-time applications. The first layer employs an IMM-Constrained Extended Kalman Filter (CEKF) framework for fault detection, while the second layer uses an Extended Kalman Filter (EKF) framework for fault diagnosis. In the first layer, four models are employed: the healthy motor model and three models with ITSC faults in each of the PMSM phases. The short-circuit resistance is estimated as an augmented state alongside system states. Upon detecting a fault in one of the phases, the second layer estimates the number of shorted turns and refines the estimated short-circuit resistance value. This study addresses three key challenges in applying the IMM framework to this problem. First, distinguishing between the healthy motor and faulty models with large short-circuit resistances is difficult due to their similar dynamic responses. This issue is resolved by imposing constraints on the estimated resistance, enabling better model separation. Second, the diversity of state variables across models is managed by zero-padding the state vector and estimation error covariance matrix, with matrix-based mode probabilities mitigating errors from augmented zeros. Third, uncertainty in the number of shorted turns is addressed in the second layer by using a secondary model set to determine the exact number of shorted turns and refine the short circuit resistance estimate. Experimental validation, conducted with a custom relay box for controlled fault injection, demonstrates the method's effectiveness in detecting and diagnosing ITSC faults using the first and second model banks, respectively.