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A Heavy-Haul Railway Corrugation Diagnosis Method Based on WPD-ASTFT and SVM

Authors :
Binghuan Xiao
Jinzhao Liu
Ziyuan Zhang
Source :
Shock and Vibration, Vol 2022 (2022)
Publication Year :
2022
Publisher :
Hindawi Limited, 2022.

Abstract

Rail corrugation in heavy-haul railway increases the contact forces between the wheel and the rail and deteriorates the rail condition. Severe corrugation affects railway operational safety. Fast diagnosis techniques allow technical personnel to perform timely maintenance and repair, preventing the quick deterioration of rail corrugation. This paper presents a heavy-haul railway corrugation diagnosis method incorporating the time-frequency analysis with machine learning methods. First, the signal is decomposed into several subsignals by wavelet packet decomposition (WPD). The paper proposes an adaptive short-time Fourier transform (ASTFT) and performs the ASTFT on the subsignals to obtain the optimal resolution time-frequency distribution and compute the corresponding entropy. The dimensionality reduction based on mean entropy is then performed for the high-dimensional data. The training and testing samples are classified using Support Vector Machine (SVM). The adaptive short-time Fourier transform (ASTFT) is incorporated with the Renyi entropy and the particle swarm optimization algorithm, which achieves a better aggregation of the time-frequency distribution and reduces the computation cost. Finally, to assist the repair work and estimate the severity of the corrugation section, the corrugation index is proposed. The corrugation indices for the determined corrugation sections are calculated to measure the severity of the corrugation. Experimental studies performed on the axle-box vertical acceleration data collected from the heavy-haul comprehensive inspection train show that the method presented by this paper achieves higher accuracy when compared with conventional feature classification methods for time-frequency analysis. The accuracy of corrugation recognition for the presented method is 93%.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
18759203
Volume :
2022
Database :
Directory of Open Access Journals
Journal :
Shock and Vibration
Publication Type :
Academic Journal
Accession number :
edsdoj.2b6cce5b779747149972d57ac57d444c
Document Type :
article
Full Text :
https://doi.org/10.1155/2022/8370796