Fault diagnosis of brushless DC motor for an aircraft actuator using a neural wavelet network

Author

Abed, W.R. ; Sharma, S.K. ; Sutton, R.

Author_Institution

Marine & Ind. Dynamic Anal. (MIDAS) Res. group, Plymouth Univ., Plymouth, UK

fYear

2013

fDate

4-5 June 2013

Firstpage

1

Lastpage

6

Abstract

Intelligent techniques (AI) have been successfully used in machines for fault diagnosis. In this paper a diagnostics approach based on discrete wavelet transform (DWT) and Neural network (NN) for stator winding inter-turn and open phase faults is presented. Simulink/Matlab is used to simulate a phase variable model of the BLDC motor with trapezoidal back - electric motive force (B-emf) under both normal and abnormal operating conditions. The NN classifies the healthy and faulty conditions by analysing the stator current and rotational speed of the motor.

Keywords

actuators; aerospace computing; aircraft control; aircraft testing; artificial intelligence; brushless DC motors; discrete wavelet transforms; fault diagnosis; neural nets; power engineering computing; stators; AI; B-emf; BLDC motor; DWT; Matlab; NN; Simulink; aircraft actuator; brushless DC motor; discrete wavelet transform; fault diagnosis; intelligent technique; neural network; neural wavelet network; open phase fault; phase variable model simulation; stator winding interturn fault; trapezoidal back-electric motive force; Brushless DC motor; fault diagnosis; neural network and wavelet transform;

fLanguage

English

Publisher

iet

Conference_Titel

Control and Automation 2013: Uniting Problems and Solutions, IET Conference on

Conference_Location

Birmingham

Electronic_ISBN

978-1-84919-710-6

Type

conf

DOI

10.1049/cp.2013.0020

Filename

6613733