Minor Fault Detection for Permanent Magnet Synchronous Motor Based on Fractional Order Model and Relative Rate of Change

Permanent magnet synchronous motor is a kind of typical nonlinear complex system. With its excellent performance such as high torque density, high efficiency and high reliability, it becomes the mainstream motor in the fields of active aircraft, electric vehicles and industrial servo drives. However, the existing fault diagnosis based on integer order model does not consider the fractional-order characteristics contained in the electromagnetic coupling and friction in the motor system, then it is difficult to effectively diagnose minor faults of the current with the residual error signal. In this paper, based on the traditional method, the state space representation based on fractional order model and the fault detection method of Kalman filter algorithm are introduced, and the secondary detection is adopted to calculate the relative change rate of typical fault feature quantity, and the experiment is verified.