Motor Fault Detection and Diagnosis Based on Negative Selection Algorithm

DOI

10.2991/icimm-15.2015.246How to use a DOI?

Keywords

artificial immune system; negative selection algorithm; fault detection; fault diagnosis

Abstract

This paper presents a motor fault diagnosis method based on negative selection algorithm. It has the structure of two-level detectors, the first level detector detecting the presence of faults and the second level detector detecting the type of faults. Therefore the first level detectors are trained by using motor normal signals, and the second level detectors are trained by using several types of fault signals. During the process of detecting, only the test results of first level detectors are abnormal, the second level detectors are activated and implement fault detection to identify fault type. In this paper, normal vibration signals of motor bearing and three types of fault signals from American Case Western Reserve University bearing fault database are used to verify the fault diagnosis method. The experimental results show that the method can effectively detect early failure and can correctly identify the fault type.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lihua Zhou
AU  - Zhongjian Dai
AU  - Yaping Dai
AU  - Linhui Zhao
PY  - 2015/07
DA  - 2015/07
TI  - Motor Fault Detection and Diagnosis Based on Negative Selection Algorithm
BT  - Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials
PB  - Atlantis Press
SP  - 1353
EP  - 1358
SN  - 2352-5401
UR  - https://doi.org/10.2991/icimm-15.2015.246
DO  - 10.2991/icimm-15.2015.246
ID  - Zhou2015/07
ER  -