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Impact of resources and monitoring effectiveness on prognostics enabled condition based maintenance policy

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  • Taiwo Joel Omoleye
  • Abdullah A. Alabdulkarim
  • Kwok L. Tsui

Abstract

In the literature, the role of prognostic information in Condition-Based Maintenance (CBM) policy has been assessed based on the assumptions of perfect condition monitoring and diagnostics. However, effective prognosis require both detection and diagnosis. This research focuses on CBM implementation from a new perspective by using an Excel-based interface integrated with ARENA® based Discrete Event Simulation (DES) to assess and analyse the impact of resources and monitoring effectiveness on the key critical phases in CBM policy. This paper seeks to understand how the influence of resources and monitoring effectiveness affect asset availability and overall cost, and to investigate the conditions under which prognostics-enabled CBM could be superior to classic CBM. Without optimisation, prognostics-enabled CBM provided superior technical benefits; however, with optimisation, overall cost effectiveness was achieved. The proposed model can provide maintenance decision makers implementing CBM with numerical evidence in assessing the benefits, and adoption of prognostics in their operation.

Suggested Citation

  • Taiwo Joel Omoleye & Abdullah A. Alabdulkarim & Kwok L. Tsui, 2019. "Impact of resources and monitoring effectiveness on prognostics enabled condition based maintenance policy," Journal of Simulation, Taylor & Francis Journals, vol. 13(4), pages 254-271, October.
    • Handle: RePEc:taf:tjsmxx:v:13:y:2019:i:4:p:254-271
    DOI: 10.1080/17477778.2018.1524269
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    Cited by:

    1. Leoni, Leonardo & De Carlo, Filippo & Tucci, Mario, 2023. "Developing a framework for generating production-dependent failure rate through discrete-event simulation," International Journal of Production Economics, Elsevier, vol. 266(C).

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