Led by certain European countries, rolling stock (railway car) maintenance technology is undergoing a paradigm shift from preventive maintenance based on the inspection period to predictive maintenance in a bid to reduce damages to railroad components that cause interruptions to railroad
operation and incur unnecessary maintenance costs. This has led to increasing demand for fault diagnosis and remaining- useful-life-prognosis technologies in order to simultaneously satisfy the need for greater reliability and lower maintenance costs to cope with faster systems. This study aimed to design the functions and architecture of a rolling stock maintenance support system that analyzes the status data collected from sensors installed at onboard and wayside in order to automatically evaluate and prognosticate the likelihood of parts failures, so as to manage railroad car parts more efficiently.
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A book published by Korail, A study on a method of calculating the lifespan of freight train axle bearings, 2014.
Korail, Development of an early failure detection and maintenance technology for core parts of the rolling stock at onboard and wayside, Land and Transport Technology Research and Development Plan, 2015.
Byeon, S-K., Kang, C-W., Sim S-B., Defect Type Prediction Method in Manufacturing Process Using Data Mining Technique, Proceedings of the Korean
Society of Industial and System Eng., Vol. 27, No. 2, 2004, pp. 10-16.
Kim, H. G., and Cho, H. S., A Production and Preventive Maintenance Policy with Two Types of Failures, Journal of the Korean Society for Quality Management, Vol. 30, No. 3, 2002, pp.53-65.