Please use this identifier to cite or link to this item:
Title: A Bayesian probabilistic approach for acoustic emission-based rail condition assessment
Authors: Wang, J
Liu, XZ
Ni, YQ 
Issue Date: 2018
Publisher: Wiley-Blackwell
Source: Computer-aided civil and infrastructure engineering, 2018, v. 33, no. 1, p. 21-34 How to cite?
Journal: Computer-aided civil and infrastructure engineering 
Abstract: The investigation described in this article aims at developing a Bayesian-based approach for probabilistic assessment of rail health condition using acoustic emission monitoring data. It comprises the following three phases: (i) formulation of a frequency-domain structural health index (SHI), via a linear transformation method, tailored to damage-sensitive frequency bandwidth (ii) establishment of data-driven reference models, using Bayesian regression about the real and imaginary parts of the SHI derived with monitoring data from the intact rail and (iii) quantitative evaluation of discrimination between the new observations representative of current rail health condition and the baseline model predictions in terms of Bayes factor. If the deviation of the new observations from the predictions is within an acceptable tolerance, no damage is flagged, and the new data are further used to update and refine the reference models. If the observations deviate substantially from the model predictions in a probabilistic sense, damage is signaled, damage severity is quantified, and damage location determined. The proposed approach is examined by using field monitoring data acquired from an instrumented railway turnout, and the coincidence between the assessment results and the actual health conditions demonstrates its effectiveness in damage detection, localization, and quantification.
ISSN: 1093-9687
EISSN: 1467-8667
DOI: 10.1111/mice.12316
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Citations as of Sep 11, 2018


Citations as of Sep 18, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.