The Degradation State Recognition of Rolling Bearing Based on GA and SVM

DOI

10.2991/meic-14.2014.123How to use a DOI?

Keywords

genetic algorithm(GA);support vector machine (SVM); rolling bearing;fault; degradation state

Abstract

In order to accurately recognize the degradation state of rolling bearing, a hybrid method combining Genetic Algorithm GA and a Support Vector Machine (SVM) was proposed,and the model for degradation state recognition of rolling bearing was constructed. Firstly the feature vectors of degradation state were extracted through the combination of GA and SVM from statistical characteristic. Then the degradation state probability distribution and historical remn ant life of rolling bearing are calculated to deter mine the optimal number of degradation state, whi ch is employed to construct the SVM model for deg radation state recognition. Finally extracted the characteristic vectors which have been optimized and deleted by GA from the test data of different degradation states, and then using the character ristic vectors as the input of SVM which parame ters has been optimized by GA to identify the degradation state of rolling bearing.The analytical results for full lifetime datasets of a certain bearing demonstrate the validity of the method.

Copyright

© 2014, 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  - Yonghe Wei
AU  - Minghua Wang
PY  - 2014/11
DA  - 2014/11
TI  - The Degradation State Recognition of Rolling Bearing Based on GA and SVM
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 547
EP  - 551
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.123
DO  - 10.2991/meic-14.2014.123
ID  - Wei2014/11
ER  -