Detection of performance deterioration of high-speed train wheels based on Bayesian dynamic model

Y. W. Wang, Yiqing Ni, X. Wang

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

1 Citation (Scopus)

Abstract

This paper presents a novel technique, in the context of Bayesian dynamic linear model (BDLM) and Bayesian forecasting, for detecting the performance deterioration of high-speed train wheels using online monitoring data of strain acquired from in-service train bogies. The BDLM is a tool for time series analysis and Bayesian forecasting enables to calculate one-step ahead forecast distribution. The change detection is carried out by checking the current observation against the current model (forecast distribution generated by the BDLM for current instant) as well as against an alternative model (whose mean value is shifted by a prescribed offset). The detection rule is that if the alternative model better fits the actual observation, a potential change is alarmed. To further determine whether the current observation is an outlier or the beginning of a change, a specific logic is developed by introducing the Bayes factors and cumulative Bayes factors. The proposed method is demonstrated by using the monitoring data acquired from an in-service high-speed train under different wheel quality conditions.
Original languageEnglish
Title of host publicationStructural Health Monitoring 2017
Subtitle of host publicationReal-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
PublisherDEStech Publications
Pages2808-2815
Number of pages8
Volume2
ISBN (Electronic)9781605953304
Publication statusPublished - 1 Jan 2017
Event11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 - Stanford University, Stanford, United States
Duration: 12 Sep 201714 Sep 2017

Conference

Conference11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
Country/TerritoryUnited States
CityStanford
Period12/09/1714/09/17

ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications

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