Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

Research about rolling element bearing fault diagnosis based on mathematical morphology and sample entropy

Authors
Lingli Cui, Xiangyang Gong, Yu Zhang
Corresponding Author
Lingli Cui
Available Online January 2016.
DOI
https://doi.org/10.2991/icsmim-15.2016.24How to use a DOI?
Keywords
mathematical morphology; pattern spectrum; sample entropy; BP neural network
Abstract

In view of the non-linear and non-stationary of the rolling element bearing fault signal, the method of mathematical morphology analysis is introduced into the rolling element bearing fault diagnosis. Multi-scale morphological transform is applied to the analysis of the bearing signals. To describe the complexity of pattern spectrum curves by using sample entropy, and its value as the input vector of the neural network is used to realize the fault pattern classification by using the back-propagation (BP) neural network. Experimental results show that this method is effective.

Copyright
© 2016, 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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Volume Title
Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
978-94-6252-157-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icsmim-15.2016.24How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Lingli Cui
AU  - Xiangyang Gong
AU  - Yu Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Research about rolling element bearing fault diagnosis based on mathematical morphology and sample entropy
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 126
EP  - 129
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.24
DO  - https://doi.org/10.2991/icsmim-15.2016.24
ID  - Cui2016/01
ER  -