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A study on the application method of RAMS analysis and reliability management system for railway vehicle components
Journal of the Korean Data & Information Science Society 2022;33:1053-64
Published online November 30, 2022;
© 2022 Korean Data and Information Science Society.

Hyunwoo Lee1 · Jaeyoung Park2

12Bigtel Inc.
Correspondence to: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22RSCD-A156010-03).
1 Vice president, Reliability division in Bigtel, Jojeong-daero, Hanam-si, Gyeonggi-do, 12930, Korea. E-mail:
2 Research engineer, Reliability division in Bigtel, Jojeong-daero, Hanam-si, Gyeonggi-do, 12930, Korea.
Received August 8, 2022; Revised September 16, 2022; Accepted September 16, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Currently, domestic railways are required to perform RAMS analysis when replacing new vehicles and parts under the Railway Safety Act. However, most of the RAMS analysis conducted in Korea is performed separately from the operator’s failure and maintenance data. Therefore, the results of the RAMS analysis are not accurately reflected in the operation results of the operator. Operators should present operational data for RAMS analysis according to the reliability management system procedures, and manufacturers should reflect the failure and maintenance data provided by the operator in the design to minimize repeated failures. Through this process, optimal maintenance using the RAMS analysis results can be performed. In this study, a method of performing RAMS analysis using maintenance and fault data of railway components was presented, and procedures for data management and RAMS analysis were presented.
Keywords : Maintenance, railway component, RAMS analysis, RCM, reliability management system.