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http://dx.doi.org/10.12989/sss.2022.30.3.303

Online railway wheel defect detection under varying running-speed conditions by multi-kernel relevance vector machine  

Wei, Yuan-Hao (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
Wang, You-Wu (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
Ni, Yi-Qing (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
Publication Information
Smart Structures and Systems / v.30, no.3, 2022 , pp. 303-315 More about this Journal
Abstract
The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.
Keywords
model optimization; multi-kernel RVM; online detection; railway wheel defect; relevance vector machine (RVM); varying running speed;
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Times Cited By KSCI : 3  (Citation Analysis)
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