A Bayesian Machine Learning Approach for Online Wheel Condition Detection Using Track-side Monitoring

Yi Qing Ni, Qiu Hu Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Online wheel condition monitoring can suffer from the stochastic wheel/rail dynamics and measurement noises. This paper aims to develop a Bayesian statistical approach for probabilistic assessment of wheel conditions using track-side monitoring. In this approach, the wheel quality-related components are first extracted from monitoring data and their Fourier amplitude spectra are normalized to obtain a set of cumulative distribution functions that characterize wheel quality information. Then a data-driven reference model is established by means of sparse Bayesian learning for modelling these characteristic functions for healthy wheels. Bayes factor is finally employed to discriminate the new observations from the reference model, with which a quantitative evaluation of wheel qualities is achieved in real time. To validate the feasibility and effectiveness, the proposed approach is examined by using strain monitoring data of rail bending acquired from a track-side monitoring system based on optical fiber sensors.

Original languageEnglish
Title of host publication2018 International Conference on Intelligent Rail Transportation, ICIRT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538675281
DOIs
Publication statusPublished - 13 Feb 2019
Event2018 International Conference on Intelligent Rail Transportation, ICIRT 2018 - Singapore, Singapore
Duration: 12 Dec 201814 Dec 2018

Publication series

Name2018 International Conference on Intelligent Rail Transportation, ICIRT 2018

Conference

Conference2018 International Conference on Intelligent Rail Transportation, ICIRT 2018
CountrySingapore
CitySingapore
Period12/12/1814/12/18

Keywords

  • optical fiber sensors
  • sparse Bayesian learning
  • track-side monitoring
  • wheel condition monitoring

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Management Science and Operations Research
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Transportation

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