Motor Online Fault Diagnosis Based on Artificial Intelligence Techniques

Author

Qiu, Chidong ; Tan, Yue ; Ren, Guang

Author_Institution

Autom. & Electr. Eng. Coll., Dalian Maritime Univ.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5819

Lastpage

5823

Abstract

This paper introduces a hybrid method of motor online fault diagnosis. Firstly, motor stator current was processed by means of fast Fourier transform, the frequency response was gained. Then the continuous frequency response was discretized for the aim of classification, the discretization process was implemented based on Kohonen neural networks. Finally, the advanced classification was implemented by means of rough set theory. Based on reduced decision table, fault diagnosis rules were found. In this paper, those classified data was measured in laboratory when motor operated under man-made fault condition. By simulating and computing, it was confirmed that the method was feasible

Keywords

artificial intelligence; electric motors; fast Fourier transforms; fault diagnosis; frequency response; power engineering computing; rough set theory; self-organising feature maps; stators; Kohonen neural networks; artificial intelligence; continuous frequency response; data classification; decision table; discretization process; fast Fourier transform; fault diagnosis rules; hybrid method; motor online fault diagnosis; motor stator current; rough set theory; Artificial intelligence; Automation; Educational institutions; Electrical engineering; Fast Fourier transforms; Fault diagnosis; Frequency response; Neural networks; Set theory; Stators; Artificial Intelligence; Fault Diagnosis; Kohonen Neural Networks; Motor; Rough Sets Theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714192

Filename

1714192