• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1357-1365
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• 2011

Reliability analysis-based maintenance system has the merit than elapsed time-based preventive maintenance system. First, it is possible to active handling of rolling stocks failures. In the past, Failure handling activities began when after increase failures rapidly. But if we can continuous manage of reliability than changing of failure tendency can know more quickly. Second, It is possible to using maintenance resources effectively. We can look forward to cost reduction to distributing of much less failures parts's maintenance resources. KORAIL builds reliability system called KTX-RCM for operating high-speed rolling stocks. But existing rolling stocks have not reliability system from the past. Fortunately, KORAIL started ERP system called KOVIS at 2007. KOVIS includes maintenance data for reliability analysis. So in this study analysis failure data for getting reliability number about existing passenger car. And we desire to maintenance connection plan.

• Journal of the Korean Society of Safety
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• v.36 no.2
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• pp.1-9
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• 2021

The 8200-unit electric locomotive, which is a high-efficiency multipurpose electric locomotive, is a German model, namely BR152 series ES64F, and it is manufactured to suit the operating conditions in Korea. Since 2003, 83 locomotives have been introduced in Korea, and they have been operating in the general railway sector for both passenger and freight transport. Although more than 15 years have passed since their first introduction, owing to the characteristics of vehicles introduced overseas, responding promptly to failures has been difficult owing to problems related to factors such as transfer of technology and procurement of parts for maintenance. Furthermore, there have been difficulties in operating the locomotives on the basis of the manufacturer-recommended time-between-overhaul (TBO) cycle. Therefore, a new TBO should be determined. To support the development of a reliability-based maintenance system, this study conducted a reliability and TBO analysis by using failure data obtained from KOVIS, and future management measures are presented.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1179-1186
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• 2009

The high-speed train(KTX) was operated in domestic since 2004 and 4years have been passed since then. At the beginning most of the KTX component maintenance period was followed by the France SNCF. It was settled by the TBO(Time Between Overhauls). But the environment and operation condition are different form each other, so the TBO maintenance period can be different in some component. Therefore new TBO is necessary based on our own circumstances. This paper introduces failure analysis case on some components to set TBO in domestic. KTX-RCM(Reliability Centered Maintenance System), MICS(Maintenance Information Computer System) and KOVIS(KORAIL ERP System) Data was investigated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.609-617
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• 2017

Railways are one of the safest and most important transportation systems in the world. On the other hand, due to the increasing complexity of the railway system and the running distance of rail vehicles, railway accidents occur continuously every year. In particular, in the case of high-speed trains and freight trains, if the function of the axle bearing is lost due to abnormal overheating of the axle box bearing, the load on the axle becomes uneven. Therefore, abnormal overheating in the train axle box bearings can cause serious accidents or derailments. For this purpose, a Hot Box Detector (HBD) was installed in the track side of a high speed line to detect abnormal overheating. This paper proposes an EWMA technique-based axle temperature monitoring method to detect abnormal overheating quickly and efficiently. A statistical design of the proposed method was also performed. The proposed method has better performance compared to the current method in the case of abnormal overheating and the performance is improved by approximately 170% at the maximum.