Fault Detection and Diagnosis Algorithm Development for PMSMs Based on the Simplified Refined Instrumental Variable for Continuous-Time Systems Algorithm

Permanent Magnet Synchronous Motors (PMSMs) are indispensable in modern industrial applications, particularly within the transportation sector, due to their exceptional efficiency and precise controllability. Nevertheless, PMSM failures can result in substantial operational disruptions and pose significant safety risks. Consequently, the development and implementation of condition monitoring (CM) and fault detection and diagnosis (FDD) methodologies have become increasingly crucial. This paper introduces an FDD approach that leverages a parameter estimation-based technique: the Simplified Refined Instrumental Variable for Continuous-time Systems (SRIVC) algorithm. It distinguishes itself from prior work by integrating first-principle mathematical modeling of PMSMs. This integration enables the derivation of physically meaningful parameter estimations. By monitoring changes in these estimated parameters, it is possible to infer three prevalent PMSM fault modes: inter-turn short circuit (ITSC), degradation of lubrication, and partial demagnetisation. The efficacy of this approach is demonstrated through simulations conducted within a MATLAB/Simulink environment.