Condition-based maintenance in the cyclic patrolling repairman problem

Havinga, Maik J.A.; Jonge, Bram de

doi:10.1016/j.ijpe.2019.09.018

Cited by 22 publications

(8 citation statements)

References 42 publications

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“…In (Havinga and de Jonge, 2020), the authors investigated a cyclic TMP, formulated as a MDP, combined with condition-based preventive maintenance. The proposed control-limit heuristic was compared with the optimal policy and with traditional corrective maintenance policies.…”

Section: Traveling Maintainer Problemmentioning

confidence: 99%

Policies for the Dynamic Traveling Maintainer Problem with Alerts

Costa¹,

Verleijsdonk²,

Voorberg³

et al. 2021

Preprint

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Companies require modern capital assets such as wind turbines, trains and hospital equipment to experience minimal downtime. Ideally, assets are maintained right before failure to ensure maximum availability at minimum maintenance costs. To this end, two challenges arise: failure times of assets are unknown a priori and assets can be part of a larger asset network. Nowadays, it is common for assets to be equipped with real-time monitoring that emits alerts, typically triggered by the first signs of degradation. Thus, it becomes crucial to plan maintenance considering information received via alerts, asset locations and maintenance costs. This problem is referred to as the Dynamic Traveling Maintainer Problem with Alerts (DTMPA). We propose a modeling framework for the DTMPA, where the alerts are early and imperfect indicators of failures. The objective is to minimize discounted maintenance costs accrued over an infinite time horizon. We propose three methods to solve this problem, leveraging different information levels from the alert signals. The proposed methods comprise various greedy heuristics that rank assets based on proximity, urgency and economic risk; a Traveling Maintainer Heuristic employing combinatorial optimization to optimize near-future costs; a Deep Reinforcement Learning (DRL) method trained to minimize the long-term costs using exclusively the history of alerts. In a simulated environment, all methods can approximate optimal policies with access to perfect condition information for small asset networks.For larger networks, where computing the optimal policy is intractable, the proposed methods yield competitive maintenance policies, with DRL consistently achieving the lowest costs.

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Section: Traveling Maintainer Problemmentioning

confidence: 99%

Policies for the Dynamic Traveling Maintainer Problem with Alerts

Costa¹,

Verleijsdonk²,

Voorberg³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, when N is determined, the optimal preventive maintenance cycle T * can be obtained by equation (10).…”

Section: Determination Of Preventive Maintenance Cycle T*mentioning

confidence: 99%

“…[6][7][8] Preventive maintenance can be further divided into two categories according to the basis of formulating maintenance strategies, namely condition-based preventive maintenance and time-based preventive maintenance. 9 According to the current information of the equipment monitored by the sensor, condition-based preventive maintenance [10][11][12][13][14][15][16] judges whether the equipment is running abnormally, and repairs it before the equipment failure occurs, thereby reducing the probability of equipment failure. Theoretically, condition-based preventive maintenance uses more advanced technical means to get a more ideal maintenance effect.…”

Section: Introductionmentioning

confidence: 99%

Optimization of equal-cycle maintenance strategy considering imperfect preventive maintenance

Ran

Zhang

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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Preventive maintenance is an important means to extend equipment life and improve equipment reliability. Traditional preventive maintenance decision-making is often based on components or the entire system, the granularity is too large and the decision-making is not accurate enough. The meta-action unit is more refined than the component or system, so the maintenance decision-making based on the meta-action unit is more accurate. Therefore, this paper takes the meta-action unit as the research carrier, considers the imperfect preventive maintenance, based on the hybrid hazard rate model, established the imperfect preventive maintenance optimization model of the meta-action unit, and the optimization solution algorithm was given for the maintenance strategy. Finally, through numerical analysis, the validity of the model is verified, and the influence of different maintenance costs on the optimal maintenance strategy and optimal maintenance cost rate is analyzed.

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“…Recently, there has been increased interest in the TRSP in the context of maintenance operations. Goel & Meisel (2013), Camci (2015), López-Santana et al (2016), Irawan et al (2017), Schrotenboer et al (2018), Nguyen et al (2019), andHavinga &De Jonge (2019) all study the problem of planning and scheduling maintenance operations for a set of geographically distributed machines that need to be performed by a fleet of service engineers. These models assume that most of these operations are known a-priori for a fixed planning horizon or that the fleet consists of only one service engineer.…”

Section: Literaturementioning

confidence: 99%