Simulation and fault diagnosis for BLDCM

Faults in BLDCM (Brushless Direct Current Motor) are difficult to avoid and may result in serious consequences. Effective FDD (fault detection and diagnosis) can improve motor reliability and avoid expensive maintenance. Therefore, the early examination and diagnosis to the motor common fault could prevent the disaster from occurring effectively, which has a great significance. Establish BLDCM simulation model by using finite element software Maxwell and Simplorer. Inject fault by means of altering the motor parameters, so the fault model is established. Make the EMD decomposition with stator phase current which obtained by the model to acquire a series of intrinsic mode function, then carry on the SPWVD time-frequency analysis to the IMF to gain the time-frequency characteristic under different fault conditions. Summarize the change law of the time-frequency characteristics which varying along with the fault degree by multi-group data. Finally, based on the proposed fault model, an intelligent optimization algorithm CPSO (Chaotic mutation based Particle Swarm Optimization) is applied to perform fault diagnosis for motor, which is capable of identifying both fault location and fault severity. The consistency of simulation results and the experimental analysis has verified the correctness of simulation model and the diagnosis method.