Open circuit fault diagnosis strategy of PMSM drive system based on grey prediction theory for industrial robot

Abstract

Permanent magnet synchronous motor (PMSM) is widely used in industrial robot joints and machine tools due to its high torque density, good controllability, and compact structure. Automatic fault diagnosis and early warning of PMSM drive system of industrial robot is the key and difficult point for the smooth development of automatic production process. In view of the problems that the traditional open circuit fault diagnosis method is not fast enough, not intelligent enough, and prone to misdiagnosis in the case of load change, an open circuit fault diagnosis and location strategy for windings and power switches of PMSM drive system based on grey prediction theory is proposed. The grey prediction method only needs to collect a small amount of motor current data and uses the grey model to estimate and predict the rule of the system. It can still diagnose and locate faults accurately in the case of load change, and compared with the traditional current detection method, the diagnosis speed is fast, and the accuracy is high. Through the theoretical analysis, simulation, and experimental results, it is verified that proposed strategy can diagnose the power switch and winding open circuit faults accurately in real time, which improves the reliability of the electric drive system in the application of industrial robot.