Bearing Fault Recognition Based on Feature Extraction and Clustering Analysis
Xin Zhang, Jianmin Zhao, Haiping Li, Fucheng Sun
Available Online March 2016.
- https://doi.org/10.2991/icmmct-16.2016.86How to use a DOI?
- Clustering analysis, bearing, fault pattern, time domain feature parameters.
- In this paper, the clustering analysis is used to distinguish bearing fault pattern. Some time domain feature parameters are extracted from vibration signal, and the combination of three feature parameters are chosen from these feature parameters for the clustering analysis. The Euclidean distance is used to calculate the distance of point-to-center. After validation, the effect of clustering analysis is effective to distinguish the bearing fault pattern, and the best combination of feature parameters for fault pattern recognition by clustering analysis is found.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xin Zhang AU - Jianmin Zhao AU - Haiping Li AU - Fucheng Sun PY - 2016/03 DA - 2016/03 TI - Bearing Fault Recognition Based on Feature Extraction and Clustering Analysis BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology PB - Atlantis Press SP - 421 EP - 426 SN - 2352-5401 UR - https://doi.org/10.2991/icmmct-16.2016.86 DO - https://doi.org/10.2991/icmmct-16.2016.86 ID - Zhang2016/03 ER -