Diagnosis of Open-Phase Faults in PM Brushless DC Motors Using Wavelet and Adaptive Fuzzy Techniques

Abstract

A discrete-time model is used to simulate performance of PM brushless DC motor drives under normal and faulty operations. Normal conditions included perfect and imperfect commutations as well as noisy operation. Faulty operation was considered an open-circuit fault on one phase of the stator windings. Current waveform of the motor-DC link is monitored and processed using continuous wavelet transform to derive suitable diagnostic indices. An adaptive neuro-fuzzy inference system (ANFIS) is trained based on indices extracted under various operating conditions in order to automate the diagnosis process. The developed ANFIS yielded a perfect diagnosis of the fault. A good agreement between simulation and experimental results is achieved.

Keywords:

• fault diagnosis
• brushless DC motors
• wavelet transform
• neuro-fuzzy systems