ANFIS-based diagnosis and location of stator interturn faults in PM brushless DC motors

Author

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

Author_Institution

Kansas State Univ., Manhattan, KS, USA

Volume

19

Issue

4

fYear

2004

Firstpage

795

Lastpage

796

Abstract

An automatic scheme for fault diagnosis and location of stator-winding interturns in permanent-magnet brushless dc motors is presented. System performances under healthy and faulty operation are obtained via a discrete-time model. Waveform of the electromagnetic torque is monitored and processed using discrete Fourier transform and short-time Fourier transform to derive proper diagnostic indices. Two adaptive neuro-fuzzy inference systems (ANFIS) are developed to automate the fault diagnosis process. Test results show an acceptable performance for ANFIS in detecting the fault.

Keywords

adaptive systems; brushless DC motors; discrete Fourier transforms; discrete time systems; electric machine analysis computing; fault diagnosis; fuzzy neural nets; inference mechanisms; permanent magnet motors; stators; torque; adaptive neuro-fuzzy inference system; discrete Fourier transform; discrete-time model; electromagnetic torque; fault diagnosis; permanent magnet brushless DC motor; short-time Fourier transform; stator interturn fault; stator winding; Adaptive systems; Brushless DC motors; Discrete Fourier transforms; Electromagnetic scattering; Fault diagnosis; Fourier transforms; Monitoring; Stators; Testing; Torque; 65; Adaptive fuzzy systems; PM; brushless dc motors; fault diagnosis; fault location; permanent-magnet;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/TEC.2004.837273

Filename

1359961