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Smart Structures and Systems Volume 30, Number 3, September 2022 , pages 303-315 DOI: https://doi.org/10.12989/sss.2022.30.3.303 |
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Online railway wheel defect detection under varying running-speed conditions by multi-kernel relevance vector machine |
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Yuan-Hao Wei, You-Wu Wang and Yi-Qing Ni
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| 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. | ||
| Key Words | ||
| model optimization; multi-kernel RVM; online detection; railway wheel defect; relevance vector machine (RVM); varying running speed | ||
| Address | ||
| (1) Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; (2) Hong Kong Branch of Chinese National Engineering Research Center on Rail Transit Electrification and Automation, Hong Kong. | ||