Feature Extraction of Bearing Status Based on Multi-Scale Bistable Stochastic Resonance Array
Xiaofei Zhang, Jingwei Gao, Lijun Song, Shuming Yang
Available Online September 2016.
- 10.2991/icmmbe-16.2016.32How to use a DOI?
- Multi-Scale Bistable Array; Stochastic Resonance; Bearing; Feature Extraction
Multi-scale bistable array (MSBA), which combines normalized scale transform, stochastic resonance effect driven by colored noise and parallel array, can be applied to weak signal detection under heavy noise. The experimental application in incipient fault feature detection of rolling element bearing has verified the effectiveness of MSBA model. This paper studies feature extraction method of rolling element bearing status degradation based on enhanced detection effect of MSBA model. Integrated features are proposed using local spectrum kurtosis and local signal-to-noise ratio of fundamental component of bearing faults. Different sizes of damages are planted on outer race of bearings for experimental validation.
- © 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 - Xiaofei Zhang AU - Jingwei Gao AU - Lijun Song AU - Shuming Yang PY - 2016/09 DA - 2016/09 TI - Feature Extraction of Bearing Status Based on Multi-Scale Bistable Stochastic Resonance Array BT - Proceedings of the6th International Conference on Mechatronics, Materials, Biotechnology and Environment (ICMMBE 2016) PB - Atlantis Press SP - 165 EP - 170 SN - 2352-5401 UR - https://doi.org/10.2991/icmmbe-16.2016.32 DO - 10.2991/icmmbe-16.2016.32 ID - Zhang2016/09 ER -