Fault Diagnosis of Locomotive Wheel-bearing Based on Wavelet Packet and MCA

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/).

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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  -