Reliability assessment of freight wagon passing through railway turnouts using adaptive Kriging surrogate model

verfasst von: Jun Lai, Kai Wang, Yan Shi, Jingmang Xu, Jiayin Chen, Ping Wang, Michael Beer
Abstract: Railway turnout (RT) is a crucial component of railway infrastructure that consists of several components. Assessing the derailment probability of freight wagons passing through the turnout is crucial for quantifying failure risks and optimizing the performance of the freight wagon-turnout system (FWTS). However, existing assessment methods often require extensive model evaluations and impose substantial computational costs. To address this issue, an efficient reliability analysis method is established for assessing the derailment risk at RTs. Firstly, a dynamic model is developed to capture the wheel-rail dynamic interaction and the numerical model is validated by field tests. Secondly, to reduce the computational cost in the reliability analysis, an efficient adaptive Kriging method based on an error stopping criteria and a learning function is adopted to estimate the failure probabilities under multiple failure modes of wheel derailments. Based on the efficient learning function and convergence criterion, accurate failure probability results can be obtained with a small number of multibody and finite element coupled dynamic simulations. Furthermore, the prediction accuracy of the proposed method in capturing random characteristics for FWTS is evaluated. Finally, the influence of the evolution of rail wear on the failure probability is further discussed.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): Southwest Jiaotong University
The University of Liverpool
Tongji University
Typ: Artikel
Journal: International Journal of Rail Transportation
Anzahl der Seiten: 20
ISSN: 2324-8378
Publikationsdatum: 20.01.2024
Publikationsstatus: Elektronisch veröffentlicht (E-Pub)
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Fahrzeugbau, Verkehr, Werkstoffmechanik
Elektronische Version(en): https://doi.org/10.1080/23248378.2024.2304000 (Zugang: Geschlossen)

BibTeX

@article{12c8babe1e0e4b18bd1261fd5caa2a6b,
title = "Reliability assessment of freight wagon passing through railway turnouts using adaptive Kriging surrogate model",
abstract = "Railway turnout (RT) is a crucial component of railway infrastructure that consists of several components. Assessing the derailment probability of freight wagons passing through the turnout is crucial for quantifying failure risks and optimizing the performance of the freight wagon-turnout system (FWTS). However, existing assessment methods often require extensive model evaluations and impose substantial computational costs. To address this issue, an efficient reliability analysis method is established for assessing the derailment risk at RTs. Firstly, a dynamic model is developed to capture the wheel-rail dynamic interaction and the numerical model is validated by field tests. Secondly, to reduce the computational cost in the reliability analysis, an efficient adaptive Kriging method based on an error stopping criteria and a learning function is adopted to estimate the failure probabilities under multiple failure modes of wheel derailments. Based on the efficient learning function and convergence criterion, accurate failure probability results can be obtained with a small number of multibody and finite element coupled dynamic simulations. Furthermore, the prediction accuracy of the proposed method in capturing random characteristics for FWTS is evaluated. Finally, the influence of the evolution of rail wear on the failure probability is further discussed.",
keywords = "Adaptive kriging model, derailment risk, multibody dynamics, Railway turnouts, reliability analysis",
author = "Jun Lai and Kai Wang and Yan Shi and Jingmang Xu and Jiayin Chen and Ping Wang and Michael Beer",
note = "Funding Information: The work was supported by National Natural Science Foundation of China (Grant No. 52122810 and 52108418), and Natural Science Foundation of Sichuan Province, China (Grant No. 2023NSFSC0398). ",
year = "2024",
month = jan,
day = "20",
doi = "10.1080/23248378.2024.2304000",
language = "English",
journal = "International Journal of Rail Transportation",
issn = "2324-8378",
publisher = "Taylor and Francis Ltd.",
}