Research about rolling element bearing fault diagnosis based on mathematical morphology and sample entropy
- 10.2991/icsmim-15.2016.24How to use a DOI?
- mathematical morphology; pattern spectrum; sample entropy; BP neural network
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.
- © 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 - 10.2991/icsmim-15.2016.24 ID - Cui2016/01 ER -