Application of CEEMD-SVM in Rolling Bearing Fault Recognition
Xiang Li, Feng Ding
Available Online December 2016.
- https://doi.org/10.2991/mcei-16.2016.304How to use a DOI?
- Complementary ensemble empirical mode decomposition; Support vector machine; Rolling bearing; Fault recognition
- 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.
- 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 -