Fault Diagnosis of Locomotive Wheel-bearing Based on Wavelet Packet and MCA
Authors
Wei-feng Yang, De-qiang He, Tao Chen, Zi-kai Yao
Corresponding Author
De-qiang He
Available Online July 2019.
- DOI
- 10.2991/eee-19.2019.27How to use a DOI?
- Keywords
- Locomotive bearing, Fault diagnosis, Wavelet packet, Morphological component analysis
- Abstract
Fault diagnosis of locomotive wheel-bearing is directly related to the locomotive performance and the safe operation of train. Owing to the fault signal of locomotive wheel-bearing being difficult to separate, the fault diagnosis method was proposed, which based on wavelet packets and morphological component analysis combined with the vibration signal characteristics of locomotive wheel-bearing. The simulation results show that the fault diagnosis of the locomotive wheel-bearing under low signal-to-noise ratio (SNR) case is achieved by wavelet packet and morphological component analysis. It provides a theoretical basis for the fault diagnosis and condition monitoring for the locomotive wheel-bearing.
- Copyright
- © 2019, 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 - Wei-feng Yang AU - De-qiang He AU - Tao Chen AU - Zi-kai Yao PY - 2019/07 DA - 2019/07 TI - Fault Diagnosis of Locomotive Wheel-bearing Based on Wavelet Packet and MCA BT - Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019) PB - Atlantis Press SP - 158 EP - 163 SN - 2352-5401 UR - https://doi.org/10.2991/eee-19.2019.27 DO - 10.2991/eee-19.2019.27 ID - Yang2019/07 ER -