Fault Feature Extraction of Rolling Bearing Based on LFK

DOI

10.2991/wartia-16.2016.134How to use a DOI?

Keywords

LCD, cross correlation coefficient, Fast Kurtogram, multi-scale entropy

Abstract

Based on multiple embedding theory, traditional multi-scale entropy is optimized by local characteristic-scale decomposition (LCD) and fast kurtogram (FK) which can be called LFK for short. In the improved method, the vibration signal of rolling bearing is decomposed by LCD and the two component signals whose cross correlation coefficient with the original signal is bigger than others are selected. FK is applied for filtering the reserved component signal and highlighting the fault feature. Multivariate multi-scale entropy is extracted from the processed signal to characterize the degradation state of rolling bearings. Compared with multi-scale entropy of the original signal, multivariate multi-scale entropy has a better performance.

Copyright

© 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/).

Download article (PDF)

TY  - CONF
AU  - He Yu
AU  - Hongru Li
AU  - Jian Sun
PY  - 2016/05
DA  - 2016/05
TI  - Fault Feature Extraction of Rolling Bearing Based on LFK
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 640
EP  - 644
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.134
DO  - 10.2991/wartia-16.2016.134
ID  - Yu2016/05
ER  -