Detection of insulation failure in BLDC motors using neuro-fuzzy systems

The paper presents a method for automatic detection of insulation failure in the stator winding of brushless DC (BLDC) motors using adaptive neuro-fuzzy systems. Healthy performance of the motor is obtained under balanced conditions through a discrete-time numerical model. Motor parameters are modified due to insulation failure across a number of turns on one phase of the stator winding. The electromagnetic torque is selected as the characteristic waveform to identify and locate the fault. One index extracted from the signal by the discrete Fourier transform (DFT) could perfectly characterize the number of faulty turns, while two other indices derived using the short-time Fourier transform (STFT) could signify the faulty phase. The diagnostic process is automated through two independent adaptive neuro-fuzzy inference systems (ANFIS) trained on simulated waveforms. Testing of ANFIS shows good performance in diagnosing and locating the fault. Experimental measurements of the output torque under normal and faulty operations show acceptable matching with simulated values, which verifies the analytical results and validates the proposed methodologies.