Opportunistic maintenance policies for multi-machine production systems with quality and availability improvement

Haohao Shi, Ji Zhang, Enrico Zio and Xufeng Zhao

Reliability Engineering and System Safety, 2023, vol. 234, issue C

Abstract: Effective maintenance policies can improve the production system performance by alleviating product quality degradation and increasing system reliability and availability. However, reliability, product quality degradation, and availability are conflicting optimization objectives and are rarely considered together in maintenance policy optimization. Aiming at optimizing these three objectives simultaneously, this paper proposes a joint model for opportunistic maintenance (OM), and product quality and availability improvement of a series multi-machine production system (MMPS). First, a condition index (CI) is developed to characterize the condition of individual machines based on monitoring data, and the corresponding threshold functions are developed to realize maintenance decision-making for individual machines. Then, considering maintenance dependence in the MMPS, based on the CI, the dual dynamic threshold functions are introduced: the preventive maintenance (PM) and the OM threshold functions, and a novel dual-dynamic-thresholds-based OM policy is developed. Whenever the CI of a machine reaches its PM threshold, all the other machines whose CI reaches the OM zone will be maintained together with this machine. Finally, a case study and comparisons are performed to show that the proposed model can simultaneously provide good production economic performance, high quality, and availability.

Keywords: Multi-machine production system; Condition indicator; Opportunistic maintenance; Quality improvement; Availability improvement (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000984

DOI: 10.1016/j.ress.2023.109183

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