A Bayesian probabilistic approach for damage detection of a population of nominally identical structures: Application to railway wheel condition assessment

Qiu Hu Zhang, Yi Qing Ni, Lu Zhou

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

Abstract

This paper proposes a Bayesian probabilistic approach to deal with the damage detection of a population of nominally identical structures. In this approach, a probabilistic reference model is first established with sparse Bayesian learning to describe structural dynamic characteristics of all nominally identical healthy structures using structural health monitoring data. Then, the conditions of the rest of structures can be identified through the examination of discrepancies between the new monitoring data and model predictions. To formulate the damage detection in a more scientific way, the discrepancies are examined by means of Bayesian hypothesis testing that allows to qualitatively and quantitatively evaluate structural conditions. To validate the feasibility and effectiveness of the proposed approach, its application to railway wheel condition assessment is presented with the use of online monitoring data collected by an optical fiber sensing track-side monitoring system.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2019
Subtitle of host publicationEnabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
EditorsFu-Kuo Chang, Alfredo Guemes, Fotis Kopsaftopoulos
PublisherDEStech Publications Inc.
Pages3508-3515
Number of pages8
ISBN (Electronic)9781605956015
Publication statusPublished - 1 Jan 2019
Event12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019 - Stanford, United States
Duration: 10 Sep 201912 Sep 2019

Publication series

NameStructural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
Volume2

Conference

Conference12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019
CountryUnited States
CityStanford
Period10/09/1912/09/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Information Management

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