Optimization of condition‐based maintenance considering partial opportunities

Condition‐based maintenance has been studied for managing the degradation of components in a system. However, components in different positions may undergo different workload, leading to imbalanced components degradation. Component reallocation is an effective method to balance the degradation of components in different positions with different degradation rates. Thus, this paper investigates the joint optimization of condition‐based maintenance and condition‐based reallocation for a non‐repairable system with multiple identical and functionally‐exchangeable components that undergo different continuous degradation processes due to different workload. We model this problem through Markov decision process, which is solved by a dynamic programming algorithm, and the objective is to provide the optimal maintenance and reallocation actions exactly for each system state, that is, the joint optimal policy, to minimize the discounted long‐run total cost. Especially, our joint policy is on component‐level and proved to be optimal. Finally, the numerical analysis shows the structural insights and effectiveness of the proposed joint policy, for which the sensitivity analysis on the degradation parameters, setup cost, reallocation cost, and downtime cost is performed. We also propose a new reallocation strategy and show the results and computation time for systems with different scales, which indicates that our model can handle systems with reasonable size.

Section: Literature Reviewmentioning

confidence: 99%

Joint optimization of condition‐based maintenance and condition‐based reallocation for a system with multiple degrading components

Wang

Yang

2023

“…We note that the results in the literature on the control limit policy of CBM are typically algorithmic‐based, see for example, Refs. 7, 8, 9, and 10. Also, the results are in closed form but numerically involved, for example, represented in the form of summations and multiple integrations with indefinite bounds, see for example, Refs.…”

Section: Introductionmentioning

confidence: 99%

Two‐threshold control limit policy in condition‐based maintenance

Hanbali

Saleh

Ullah

2022

In this paper, we consider a component exhibiting a continuous monotone degradation modeled as a Gamma process. The maintenance policy is of condition‐based type with periodic inspections. It follows the two threshold control limit policy with a preventive maintenance limit, a switching limit, and a given failure limit. Our interest is to minimize the annual expected maintenance cost subject to a constraint on the average availability. The decision variables are the preventive limit, the switching limit, and the inspections schedule. We derive exact results for the key performance indicators, but since they are numerically involved, we propose a simple, that is, involving necessary spreadsheet calculations, accurate approximation. We apply our model to a case from the process industry, namely, a carbon steel hydrogen dryer consisting of a cylindrical pressure vessel. This vessel suffers from corrosion with thickness growth modeled as a Gamma process. The optimization results show that although the annual maintenance cost increases with the mean and the coefficient of variation of the yearly corrosion thickness, its mean has a higher impact on cost. Based on the cost sensitivity analysis, we find that the impact of a vast increase of the corrective maintenance cost for the same inspection and preventive costs can be mitigated by appropriately reducing the corrective maintenance probability. Finally, we empirically show that the two threshold policy achieves an annual cost savings up to 12.3%$12.3\%$ compared to the single threshold policy.

“…proposed a CBPM strategy in which PM is performed when the deterioration level reaches a specified threshold, and an overhaul is performed when the defective product level reaches a specified threshold. Truong‐Ba et al 34 . proposed a CBPM strategy with consideration of effects of opportunity occurrences and durations.…”

Section: Introductionmentioning

confidence: 99%

“…Cheng et al 33 proposed a CBPM strategy in which PM is performed when the deterioration level reaches a specified threshold, and an overhaul is performed when the defective product level reaches a specified threshold. Truong-Ba et al 34 proposed a CBPM strategy with consideration of effects of opportunity occurrences and durations. uit het Broek et al 35 integrated CBPM strategies with production issues to enhance manufacturers' production system management for earning more profits.…”

Section: Introductionmentioning

confidence: 99%

Condition‐based preventive maintenance with a yield rate threshold for deteriorating repairable systems

Huang

Fang

Wijaya

2022

Repairable systems deteriorate with age and usage. In order to maintain acceptable reliability and prevent sudden failures, preventive maintenance (PM) is often applied to such systems. Scheduled PM actions can improve system availability and minimize losses due to breakdowns and failures. However, condition‐based PM is considered more relevant to system status than age‐based PM for deteriorating repairable systems. In this study, we consider the production yield rate as the condition variable when determining the optimal PM schedule. A non‐homogeneous Poisson process is used to describe the system deterioration, and the concept of system effective (virtual) age is also considered to make the proposed model more realistic. Three different condition‐based PM strategies are presented to provide more diverse choices for decision makers in terms of solving problems based on the situation at hand. The results show that the optimal yield rate threshold can reduce system failure while maintaining acceptable system availability and product quality with affordable costs due to a proper PM schedule. Furthermore, the results of the numerical application showed that the maintenance and penalty costs are the most sensitive to the total cost, where the adoption of the proposed PM strategies are more profitable when related costs are low.