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A study on reliability centered maintenance planning of a standard electric motor unit subsystem using computational techniques
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  • A study on reliability centered maintenance planning of a standard electric motor unit subsystem using computational techniques
  • A study on reliability centered maintenance planning of a standard electric motor unit subsystem using computational techniques
저자명
Bae. Chul-Ho,Koo. Tae-Yoon,Son. Young-Tak,Park. Ky-Jun,Jung. Jong-Deok,Han. Seok-Youn,Suh. Myung-Won
간행물명
Journal of mechanical science and technology
권/호정보
2009년|23권 4호|pp.1157-1168 (12 pages)
발행정보
대한기계학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

The design and manufacture of urban transportation applications has been necessarily complicated in order to improve its safety. Urban transportation systems have complex structures that consist of various electric, electronic, and mechanical components, and the maintenance costs generally take up approximately 60% of the total operational costs. Therefore, it is essential to establish a maintenance plan that takes into account both safety and cost. In considering safety and cost limitations, this research introduces an advanced reliability centered maintenance (RCM) planning method using computational techniques, and applies the method to a standard electric motor unit (EMU) subsystem. First, this research devises a maintenance cost function that can reflect the current operating conditions, and maintenance characteristics, of components by generating essential cost factors. Second, a reliability growth analysis (RGA) is performed, using the Army Material Systems Analysis Activity (AMSAA) model, to estimate reliability indexes such as failure rate, and mean time between failures (MTBF), of a standard EMU subsystem, and each individual component Third, two optimization processes are performed to ascertain the optimal maintenance reliability of each component in the standard EMU subsystem. Finally, this research presents the maintenance time of each component based on the optimal maintenance reliability provided by optimization processes and reliability indexes provided by the RGA method.