- Volume 20 Issue 4
DOI QR Code
Study on the Maintenance Interval Decisions for Life expectancy in Railway Turnout clearance Detector
철도 분기기 밀착검지기 Life expectancy의 유지보수 주기 결정에 관한 연구
- Jang, ByeongMok (Department of Railway Electrical & Signaling Engineering, Graduate School of Railway, Seoul National University of Science and Technology) ;
- Lee, Jongwoo (Department of Railway Electrical & Signaling Engineering, Graduate School of Railway, Seoul National University of Science and Technology)
- Received : 2017.08.21
- Accepted : 2017.08.26
- Published : 2017.08.31
Railway turnout systems are one of the most important systems in a railway and abnormal turnout systems can cause serious accidents. To detect an abnormal state of a turnout, turnout clearance detectors are widely used. These devices consider a failure of a turnout clearance detectors to be a failure of the turnout system, that could hinder train operations. Analysis of turnout clearance detector failures is very important to ensure normal train operation. We categorized failures of detectors into four groups to identify failure characteristics of the 140 detectors, which are composed of main line detectors (A), side tracks (B), detectors that are in operation more than 80 times a day (C) and detectors that are in operation fewer than 10 times per day. Failures of detectors have mainly been caused in the control part, in the cables and sensors; failures are classified into four groups (A, B, C and D). We have tried to find failure density distributions for each type of failures, inferring the parameter distributions a priori. Finally, using the Bayesian inference we proposed a maintenance time for control parts through the mean time of the detector, life and the life expectancy.
- K.H. Kang (2005) Bayesian statistic, Free Academy, Paju-si, Gyeanggi-do, pp. 77-80.
- W.C. Kim (2012) Matheaticl statistics, Minyoungsa, seoul oksu-dong, pp. 245-251.
- K.S. Sun (2006) MINITAB reliability analysis, eretec, pp. 31-47.
- S.H. Shin (2003) Weibull chart and acceleratiom exam, scince and techndogy, Gunpo-si, Gyeonggi-do, pp. 7-24.
- S.H. LTD (2012) Adherencr detctor manual, Se-haw, Yuseong-gu, Dejeon, pp. 8-10.
- D.H. Kim (2013) R with Monte Carlo Bayesian reasoning system performance, Free Acadmy, Paju-si, Gyeonggi-do, pp. 23-32.
- B.M. Jang (2013) A study on the life prediction of election point machine using Bayesian, Master of Science in Engineering, Seoul National University.
- S.U. Kim (2008) At-site low flow frequency analysis using Bayesian MCMC: theoretical background and construction of prior distribution, Korea Water Resources Institute, 41(1), pp. 35-47.