Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

Bearing Fault Recognition Based on Feature Extraction and Clustering Analysis

Authors
Xin Zhang, Jianmin Zhao, Haiping Li, Fucheng Sun
Corresponding Author
Xin Zhang
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.86How to use a DOI?
Keywords
Clustering analysis, bearing, fault pattern, time domain feature parameters.
Abstract
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.

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.86How to use a DOI?
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  -