Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019)

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
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference on Electrical and Electronic Engineering (EEE 2019)
Part of series
Advances in Engineering Research
Publication Date
July 2019
ISBN
978-94-6252-754-6
ISSN
2352-5401
DOI
https://doi.org/10.2991/eee-19.2019.27How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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  - 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  - https://doi.org/10.2991/eee-19.2019.27
ID  - Yang2019/07
ER  -