Diagnosis of switch open-circuit fault in PM brushless DC motor drives

Author

Awadallah, M.A. ; Morcos, M.M.

Author_Institution

Dept. of Electr. & Comput. Eng., Kansas State Univ., Manhattan, KS, USA

fYear

2003

fDate

7-9 May 2003

Firstpage

69

Lastpage

73

Abstract

An adaptive neuro-fuzzy inference system (ANFIS) is developed to diagnose open switch faults in the inverter bridge of PM brushless DC motor drives. Performance of the drive system under normal and faulty conditions is obtained through a discrete-time model. The motor DC-link current is monitored over one electrical cycle under healthy and faulty operations. The time-domain waveform is processed using wavelet transform, and suitable indices are derived to train ANFIS. Testing of the diagnosing system shows the effectiveness of the proposed methodology.

Keywords

DC motor drives; bridge circuits; brushless DC motors; discrete time systems; electric machine analysis computing; fault diagnosis; fuzzy neural nets; inference mechanisms; invertors; learning (artificial intelligence); permanent magnet motors; time-domain analysis; wavelet transforms; PM brushless DC motor drives; adaptive neuro-fuzzy inference system; discrete-time model; electrical cycle; faulty conditions; feature extraction; inverter bridge; machine fault diagnosis; motor DC-link current monitoring; normal conditions; open switch faults; switch open-circuit fault diagnosis; time-domain waveform; training; wavelet transform; Adaptive systems; Bridges; Brushless DC motors; DC motors; Discrete wavelet transforms; Drives; Fault diagnosis; Inverters; Switches; Time domain analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering, 2003 Large Engineering Systems Conference on

Print_ISBN

0-7803-7863-6

Type

conf

DOI

10.1109/LESCPE.2003.1204682

Filename

1204682