Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

Application of CEEMD-SVM in Rolling Bearing Fault Recognition

Authors
Xiang Li, Feng Ding
Corresponding Author
Xiang Li
Available Online December 2016.
DOI
https://doi.org/10.2991/mcei-16.2016.304How to use a DOI?
Keywords
Complementary ensemble empirical mode decomposition; Support vector machine; Rolling bearing; Fault recognition
Abstract
To extract effective fault features from the nonstationary vibration signals of rolling bearing, this paper provides a fault recognition method by using complementary ensemble empirical mode decomposition (CEEMD) and support vector machine (SVM). Firstly, CEEMD is applied to process vibration signals of rolling bearing. Then some time domain features of the first several intrinsic mode functions (IMFs) are calculated to construct feature set, and it is utilized as the input of SVM. Finally, different fault states of rolling bearing are classified through SVM. The experiment results indicate that the presented method is effective, and it can significantly improve the classification accuracy of fault recognition.
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Proceedings
2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-282-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/mcei-16.2016.304How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiang Li
AU  - Feng Ding
PY  - 2016/12
DA  - 2016/12
TI  - Application of CEEMD-SVM in Rolling Bearing Fault Recognition
BT  - 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 1497
EP  - 1501
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.304
DO  - https://doi.org/10.2991/mcei-16.2016.304
ID  - Li2016/12
ER  -