Search

Recent studies have demonstrated the economic benefit of exploiting partial opportunities for maintenance, which arise from the reduction of costs of doing partial maintenance utilizing the opportunity given. A typical example is an externally induced production stop with a duration that may not be sufficient to a complete maintenance activity. This paper combines partial opportunities and condition‐based maintenance (CBM) strategies and proposes an innovative maintenance optimization method considering time‐varying economic conditions. This scenario naturally fits a broad range of assets with a finite design life (including long‐life machinery and infrastructure), operating under variable economic conditions and usage‐intensities. The maintenance optimization problem is formulated in this study as a finite‐horizon Markov decision process, where the randomly occurring opportunities are accounted for by augmenting the time‐varying, decision‐dependent transition probabilities. A dynamic programming approach is subsequently used to obtain the optimal CBM policy, consisting of time‐varying thresholds on equipment condition and the cost of conducting maintenance during the arrived opportunity. A case study of such a maintenance policy for an induced draft fan of a sugar production system located in Queensland, Australia, is undertaken to evaluate the benefits of the proposed methodology against more traditional approaches that neglect opportunities or consider only replete opportunity durations. [ABSTRACT FROM AUTHOR]