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.
[1]
J. Sottile,et al.
An on-line method to detect incipient failure of turn insulation in random-wound motors
,
1993
.
[2]
N.A Demerdash,et al.
Dynamic Modeling of Brushless dc Motors for Aerospace Actuation
,
1980,
IEEE Transactions on Aerospace and Electronic Systems.
[3]
T.G. Habetler,et al.
Insulation failure detection in the stator windings of ASD-driven induction machines using standard deviation of line currents
,
1998,
Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).
[4]
T. G. Habetler,et al.
Insulation failure prediction in AC machines using line-neutral voltages
,
1998
.