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

Yuan Hao Wei, You Wu Wang, Yi Qing Ni

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Pages (from-to)303-315
Number of pages13
JournalSmart Structures and Systems
Issue number3
Publication statusPublished - Sept 2022


  • model optimization
  • multi-kernel RVM
  • online detection
  • railway wheel defect
  • relevance vector machine (RVM)
  • varying running speed

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Online railway wheel defect detection under varying running-speed conditions by multi-kernel relevance vector machine'. Together they form a unique fingerprint.

Cite this