Mathematics (Jul 2023)
An Optimal Opportunistic Maintenance Planning Integrating Discrete- and Continuous-State Information
Abstract
Information-driven group maintenance is crucial to enhance the operational availability and profitability of diverse industrial systems. Existing group maintenance models have primarily concentrated on a single health criterion upon maintenance implementation, where the fusion of multiple health criteria is rarely reported. However, this is not aligned with actual maintenance planning of multi-component systems on many occasions, where multi-source health information can be integrated to support robust decision making. Additionally, how to improve maintenance effectiveness through a scientific union of both scheduled and unscheduled maintenance remains a challenge in group maintenance. This study addresses these research gaps by devising an innovative multiple-information-driven group replacement policy for serial systems. In contrast to existing studies, both discrete-state information (hidden defect) and continuous degradation information are employed for group maintenance planning, and scheduled postponed maintenance and unscheduled opportunistic maintenance are dynamically integrated for the first time to mitigate downtime loss. To be specific, inspections are equally spaced to reveal system health states, followed by the multi-level replacement implemented when either (a) the degradation of the continuously degrading unit reaches a specified threshold, or (b) the age of the multi-state unit since the defect’s identification reaches a pre-set age (delayed replacement). Such scheduling further enables the implementation of multi-source opportunistic replacement to alleviate downtime. The Semi-Markov Decision Process (SMDP) is utilized for the collaborative optimization of continuous- and discrete-state thresholds, so as to minimize the operational costs. Numerical experiments conducted on the critical structure of circulating pumps verify the model’s applicability.
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