Skip to main navigation Skip to search Skip to main content

Fault diagnosis and location of brushless DC motor system based on wavelet transform and artificial neural network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The reliability of Electro-mechanical Actuator (EMA) is extremely important in industrial, commercial, aerospace, and military applications. Fault diagnosis and location of the brushless DC motor (BLDCM) system used in the EMA offer a means of improving reliability and security of the EMA. In this paper normal model as well as three fault models of the BLDCM system, which are stator winding inter-turn short circuit fault model, open-switch fault model and open-winding fault model, are developed. Performance characteristics under the faulty conditions are studied through simulation. Using Wavelet Transform (WT) and Artificial Neural Network (ANN), fault diagnosis and location method of BLDCM system is developed. Simulation results demonstrate the validity of the proposed method.

Original languageEnglish
Title of host publication2010 International Conference on Electrical Machines and Systems, ICEMS2010
Pages1048-1052
Number of pages5
StatePublished - 2010
Event2010 International Conference on Electrical Machines and Systems, ICEMS2010 - Incheon, Korea, Republic of
Duration: 10 Oct 201013 Oct 2010

Publication series

Name2010 International Conference on Electrical Machines and Systems, ICEMS2010

Conference

Conference2010 International Conference on Electrical Machines and Systems, ICEMS2010
Country/TerritoryKorea, Republic of
CityIncheon
Period10/10/1013/10/10

Fingerprint

Dive into the research topics of 'Fault diagnosis and location of brushless DC motor system based on wavelet transform and artificial neural network'. Together they form a unique fingerprint.

Cite this