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 language | English |
|---|---|
| Pages (from-to) | 202-210 |
| Number of pages | 9 |
| Journal | Engineering Failure Analysis |
| Volume | 96 |
| DOIs | |
| Publication status | Published - 2019 Feb 1 |
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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 journal › Article
}
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
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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 -