Failure detection and diagnosis system of BLDCM with dynamic load

Failure detection and diagnosis system of permanent magnet brushless DC motor (BLDCM) takes an important role in improvement of the reliability for BLDCM system. But external dynamic load of the motor may affect the validity of the fault diagnosis and location. In this paper, normal models as well as five fault models of the BLDCM system are developed and the performance under the fault conditions are studied in simulation. Based on the above discussion, the effect of the dynamic load on the failure detection and diagnosis system are presented. And using the Artificial Neural Network (ANN), the diagnosis of BLDCM system with dynamic load is developed as well. Finally the simulation results are given to verify the effectiveness and usability of the proposed method.