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

DOI

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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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  - 10.2991/icsmim-15.2016.24
ID  - Cui2016/01
ER  -