Research Article
Study on Rolling Bearing On-Line Health Status Estimation Approach Based on Vibration Signals
@INPROCEEDINGS{10.1007/978-3-319-73317-3_15, author={Yulong Ying and Jingchao Li and Jing Li and Zhimin Chen}, title={Study on Rolling Bearing On-Line Health Status Estimation Approach Based on Vibration Signals}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={Rotating machines Rolling bearing Vibration signals Health status}, doi={10.1007/978-3-319-73317-3_15} }- Yulong Ying
Jingchao Li
Jing Li
Zhimin Chen
Year: 2018
Study on Rolling Bearing On-Line Health Status Estimation Approach Based on Vibration Signals
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_15
Abstract
As the rolling bearing vibration signal is of nonlinear and nonstationary characteristics, the condition-indicating information distributed in the rolling bearing vibration signal is complicated, and using traditional time domain and frequency domain approaches cannot easily make an accurate estimation for the rolling bearing health status. In this paper, a simple and efficient fault diagnostic approach was proposed to accommodate to the requirements of both real-time monitoring and accurate estimation of fault type as well as severity. Firstly, a four-dimensional feature extraction algorithm using entropy and Holder coefficient theories was developed to extract the characteristic vector from the vibration signals, and secondly a gray relation algorithm was employed for achieving bearing fault pattern recognition intelligently. The experimental study have illustrated the proposed approach can efficiently and effectively improve the fault diagnostic performance compared with the existing artificial intelligent methods, and can be suitable for on-line health status estimation.
