Reliability-Centered maintenance of road junction transport assets

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a study of reliability-centered maintenance (RCM) which is conducted on the key sub-assets of a newly constructed road junction infrastructure in Nigeria. Road junctions are originators of more than 70 percent of road traffic congestion and also account for high accident rates. The traditional methods of reliability assurance used in the highway industry such as reactive maintenance and routine maintenance are often inadequate to meet the round the clock usage demands of these assets, thus the consideration for the application of a systematic RCM process for maintaining the system function by selecting and applying effective preventive maintenance (PM) tasks. The classical RCM methodology, a type of RCM, which has a top down, zero based approach for maintenance analysis, is implemented in the study. It uses an approach that critically develops and analyses thoroughly a preventive and continuous maintenance strategy in a new circumstance with an environment of uncertainty and limited operating data. The case-based reasoning cycle (i.e. retrieve, reuse, revise and retain) has been applied in the RCM approach with real time data obtained from a UK based network maintenance management system for highway infrastructure. The implementation of the classical RCM is successful in its application of various PM policies assigned to the assets and it shows that its application in the highway industry could reduce excessive maintenance backlog and frequent reactive maintenance by the effective optimisation of its PM intervals.

Original languageEnglish
JournalCivil-Comp Proceedings
Volume108
StatePublished - 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© Civil-Comp Press, 2015.

Keywords

  • Failure mode effect analysis
  • Preventive maintenance
  • Reliability-centered maintenance
  • Road junction
  • Transport assets

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence

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