Identification of Abnormal Wheel and Rail Wear Faults Based on Short-time Fourier Transforms

DOI

10.2991/978-94-6463-610-9_30How to use a DOI?

Keywords

wheel polygon; rail wave abrasion; short-time Fourier transform; spatial spectrum

Abstract

Wheel polygon and rail wave abrasion in railroad wheel-rail system are two common abnormal wheel-rail abrasion faults, which can easily affect the safety and stability of train operation. In order to understand the current situation of wheel-rail abnormal wear, and to detect and distinguish these two kinds of abnormal faults, this paper proposes a time-frequency analysis method based on short-time Fourier transform (STFT). At the same time, considering that the train is usually running under variable speed conditions, it is proposed to use the spatial spectrum instead of the time spectrum to collect and process the acceleration data of the train axlebox, and then use the STFT to analyze the time-frequency analysis of the axlebox vertical vibration acceleration signal. At the same time, this paper deduces the calculation formulae for the wavelength of rail wave abrasion and the order of wheel polygon under train variable speed conditions, and quantitatively identifies the physical characteristics of the faults by collecting the frequency amplitude of the spectrogram of the segmented signals. The results show that the accuracy of the Simpack simulation test set reaches more than 92%, and the research results provide a new technical means for the diagnosis of railroad faults.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Le-peng Zhou
AU  - Xiao-jie Sun
PY  - 2024
DA  - 2024/12/16
TI  - Identification of Abnormal Wheel and Rail Wear Faults Based on Short-time Fourier Transforms
BT  - Proceedings of the 2024 International Conference on Rail Transit and Transportation (ICRTT 2024)
PB  - Atlantis Press
SP  - 266
EP  - 273
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-610-9_30
DO  - 10.2991/978-94-6463-610-9_30
ID  - Zhou2024
ER  -