Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81663
Title: Wayside detection of wheel minor defects in high-speed trains by a Bayesian blind source separation method
Authors: Liu, XZ 
Xu, C 
Ni, YQ 
Keywords: Wheel minor defect
High-speed train
Online wayside detection
Bayesian blind source separation
FBG sensor array
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Sensors, 2 Sept. 2019, v. 19, no. 18, 3981, p. 1-16 How to cite?
Journal: Sensors 
Abstract: For high-speed trains, out-of-roundness (OOR)/defects on wheel tread with small radius deviation may suffice to give rise to severe damage on both vehicle components and track structure when they run at high speeds. It is thus highly desirable to detect the defects in a timely manner and then conduct wheel re-profiling for the defective wheels. This paper presents a wayside fiber Bragg grating (FBG)-based wheel condition monitoring system which can detect wheel tread defects online during train passage. A defect identification algorithm is developed to identify potential wheel defects with the monitoring data of rail strain response collected by the devised system. In view that minor wheel defects can only generate anomalies with low amplitude compared with the wheel load effect, advanced signal processing methods are needed to extract the defect-sensitive feature from the monitoring data. This paper explores a Bayesian blind source separation (BSS) method to decompose the rail response signal and to obtain the component that contains defect-sensitive features. After that, the potential defects are identified by analyzing anomalies in the time history based on the Chauvenet's criterion. To verify the proposed defect detection method, a blind test is conducted using a new train equipped with defective wheels. The results show that all the defects are identified and they concur well with offline wheel radius deviation measurement results. Minor defects with a radius deviation of only 0.06 mm are successfully detected.
URI: http://hdl.handle.net/10397/81663
EISSN: 1424-8220
DOI: 10.3390/s19183981
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Liu, X.-Z.; Xu, C.; Ni, Y.-Q. Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method. Sensors 2019, 19, 3981, 1-16 is available at https://dx.doi.org/10.3390/s19183981
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