Probabilistic model forecasting for rail wear in seoul metro based on bayesian theory

Min Chul Jeong, Seung Jung Lee, Kyunghwa Cha, Goangseup Zi, Jun g Sik Kong

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A safe and reliable railway operation requires an organic and systematic approach to railway maintenance. Despite the importance of timely and valid track maintenance and applicability of inspected data to the optimum track management process, inspected wear data inspected by a railway inspection system in Korea have not been utilized for decision making of maintenance scenario, but just accumulated. Moreover, the process of inspecting wear data includes some uncertainties, probabilistic-based models have more reasonable application in field. This can be accomplished by developing probabilistic-based stochastic model considering uncertainties for the prediction of rail wear using inspected data. This paper reports on the development and verification of a probabilistic forecasting model for rail wear progress. This developed forecasting model utilizes the particle filter method concept based on Bayesian theory and real inspected wear data of Seoul Metro are applied to verify the model.

Original languageEnglish
Pages (from-to)202-210
Number of pages9
JournalEngineering Failure Analysis
Volume96
DOIs
Publication statusPublished - 2019 Feb 1

Fingerprint

Rails
Wear of materials
Stochastic models
Inspection
Decision making
Statistical Models
Uncertainty

Keywords

  • Irregularity
  • Life cycle performance
  • Particle filter
  • Rail wear
  • Time series analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

Cite this

Probabilistic model forecasting for rail wear in seoul metro based on bayesian theory. / Jeong, Min Chul; Lee, Seung Jung; Cha, Kyunghwa; Zi, Goangseup; Kong, Jun g Sik.

In: Engineering Failure Analysis, Vol. 96, 01.02.2019, p. 202-210.

Research output: Contribution to journalArticle

@article{d9acf7d55bee433ea1d6b63ed89ca69e,
title = "Probabilistic model forecasting for rail wear in seoul metro based on bayesian theory",
abstract = "A safe and reliable railway operation requires an organic and systematic approach to railway maintenance. Despite the importance of timely and valid track maintenance and applicability of inspected data to the optimum track management process, inspected wear data inspected by a railway inspection system in Korea have not been utilized for decision making of maintenance scenario, but just accumulated. Moreover, the process of inspecting wear data includes some uncertainties, probabilistic-based models have more reasonable application in field. This can be accomplished by developing probabilistic-based stochastic model considering uncertainties for the prediction of rail wear using inspected data. This paper reports on the development and verification of a probabilistic forecasting model for rail wear progress. This developed forecasting model utilizes the particle filter method concept based on Bayesian theory and real inspected wear data of Seoul Metro are applied to verify the model.",
keywords = "Irregularity, Life cycle performance, Particle filter, Rail wear, Time series analysis",
author = "Jeong, {Min Chul} and Lee, {Seung Jung} and Kyunghwa Cha and Goangseup Zi and Kong, {Jun g Sik}",
year = "2019",
month = "2",
day = "1",
doi = "10.1016/j.engfailanal.2018.10.001",
language = "English",
volume = "96",
pages = "202--210",
journal = "Engineering Failure Analysis",
issn = "1350-6307",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Probabilistic model forecasting for rail wear in seoul metro based on bayesian theory

AU - Jeong, Min Chul

AU - Lee, Seung Jung

AU - Cha, Kyunghwa

AU - Zi, Goangseup

AU - Kong, Jun g Sik

PY - 2019/2/1

Y1 - 2019/2/1

N2 - A safe and reliable railway operation requires an organic and systematic approach to railway maintenance. Despite the importance of timely and valid track maintenance and applicability of inspected data to the optimum track management process, inspected wear data inspected by a railway inspection system in Korea have not been utilized for decision making of maintenance scenario, but just accumulated. Moreover, the process of inspecting wear data includes some uncertainties, probabilistic-based models have more reasonable application in field. This can be accomplished by developing probabilistic-based stochastic model considering uncertainties for the prediction of rail wear using inspected data. This paper reports on the development and verification of a probabilistic forecasting model for rail wear progress. This developed forecasting model utilizes the particle filter method concept based on Bayesian theory and real inspected wear data of Seoul Metro are applied to verify the model.

AB - A safe and reliable railway operation requires an organic and systematic approach to railway maintenance. Despite the importance of timely and valid track maintenance and applicability of inspected data to the optimum track management process, inspected wear data inspected by a railway inspection system in Korea have not been utilized for decision making of maintenance scenario, but just accumulated. Moreover, the process of inspecting wear data includes some uncertainties, probabilistic-based models have more reasonable application in field. This can be accomplished by developing probabilistic-based stochastic model considering uncertainties for the prediction of rail wear using inspected data. This paper reports on the development and verification of a probabilistic forecasting model for rail wear progress. This developed forecasting model utilizes the particle filter method concept based on Bayesian theory and real inspected wear data of Seoul Metro are applied to verify the model.

KW - Irregularity

KW - Life cycle performance

KW - Particle filter

KW - Rail wear

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=85054923625&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054923625&partnerID=8YFLogxK

U2 - 10.1016/j.engfailanal.2018.10.001

DO - 10.1016/j.engfailanal.2018.10.001

M3 - Article

AN - SCOPUS:85054923625

VL - 96

SP - 202

EP - 210

JO - Engineering Failure Analysis

JF - Engineering Failure Analysis

SN - 1350-6307

ER -