A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

Zhu, Q Qiushi; Peng, Hao; Timmermans, Blr Bas; Houtum, Geert‐Jan van

doi:10.1016/j.ijpe.2017.07.014

Cited by 31 publications

(39 citation statements)

References 38 publications

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“…In addition, unscheduled downs may occur due to failures in components. At both scheduled and unscheduled downs, maintenance can be performed at each of the components for which a periodic or condition based maintenance policy is selected (the single-item models per component have also been published as Zhu et al, 2016Zhu et al, , 2017. The long term average costs per time unit can be evaluated for each of the components for a given τ. Summing those costs for all components and adding joint setup costs (translated to costs per time unit) leads to the total costs per time unit for the complete asset.…”

Section: Literaturementioning

confidence: 99%

Design of multi-component periodic maintenance programs with single-component models

Arts

Basten

2018

IISE Transactions

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Capital assets, such as wind turbines and ships, require maintenance throughout their long life times. Assets usually need to go down to perform maintenance and such downs can be either scheduled or unscheduled. Since different components in an asset have different maintenance policies, it is key to have a maintenance program in place that coordinates the maintenance policies of all components, so as to minimize costs associated with maintenance and downtime. Single component maintenance policies have been developed for decades, but such policies do not usually allow coordination between different components within an asset. We study a periodic maintenance policy and a condition based maintenance policy in which the scheduled downs can be coordinated between components. In both policies, we assume that at unscheduled downs, a minimal repair is performed to keep unscheduled downtime as short as possible. Both policies can be evaluated exactly using renewal theory, and we show how these policies can be used as building blocks to design and optimize maintenance programs for multi-component assets.

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Section: Literaturementioning

confidence: 99%

Design of multi-component periodic maintenance programs with single-component models

Arts

Basten

2018

IISE Transactions

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“…This approximation is justified by the Palm-Khintchine theorem (Khinchin, 1956), which states that even if the failure times of some systems do not follow exponential distributions, the superposition of a sufficiently large number of independent renewal processes behaves asymptotically like a Poisson process. Arts and Basten (2018) build further on Zhu et al (2016Zhu et al ( , 2017, but they only consider scheduled maintenance opportunities (excluding unscheduled opportunities). Furthermore, Arts and Basten (2018) assume that at a scheduled opportunity, the system is restored to a perfect condition (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

“…for δ(p) 1 , we consider c so pm = 1000, c uso pm = 2000 and c cm = 300000, whereas, for δ(p) 2 , we consider c so pm = 26500, c uso pm = 28800 and c cm = 75500. The choice for the preventive maintenance cost at SOs and USOs in the second cost structure is common in the lithography industry (see Zhu et al (2017)). Based on Figure 3, we can conclude that, under both cost structures, significant costs can be saved by improving the probability of executing a perfect preventive maintenance (e.g., by training).…”

Section: Influence Of Imperfect Maintenancementioning

confidence: 99%

Condition-based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities

2019

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Motivated by the cost savings that can be obtained by sharing resources in a network context, we consider a stylized, yet representative, model for the coordination of maintenance and service logistics for a geographic network of assets. Capital assets, such as wind turbines in a wind park, require maintenance throughout their long lifetimes. Two types of preventive maintenance are considered: planned maintenance at periodic, scheduled opportunities, and opportunistic maintenance at unscheduled opportunities.The latter type of maintenance arises due to the network context: when an asset in the network fails, this constitutes an opportunity for preventive maintenance for the other assets in the network.So as to increase the realism of the model at hand and its applicability to various sectors, we consider the option of not-deferring and of deferring planned maintenance after the occurrence of opportunistic maintenance. We also assume that preventive maintenance may not always restore the condition of the system to 'as good as new'. By formulating this problem as a semi-Markov decision process, we characterize the optimal policy as a control limit policy (depending on the remaining time until the next planned maintenance) that indicates on the one hand when it is optimal to perform preventive maintenance and on the other hand when maintenance resources should be shared if an opportunity in the network arises. In order to facilitate managerial insights on the effect of each parameter on the cost, we provide a closed-form expression for the long-run rate of cost for any given control limit policy (depending on the remaining time until the next planned maintenance) and compare the costs (under the optimal policy) to those of sub-optimal policies that neglect the opportunity for resource sharing. We illustrate our findings using data from the wind energy industry.airports require maintenance throughout their (long) lifetimes. Such capital assets are crucial to the primary processes of their users/operators and unexpected failures may have very significant negative impacts and even life threatening consequences. In order to avoid or to minimize failures, asset owners perform preventive maintenance activities, with the objective to retain or to restore a system back to a satisfactory operating condition. The costs of both these maintenance activities, and of their respective unscheduled downtimes, represent one of the key drivers of an organization's total costs. Such maintenance costs constitute up to 70% of the total value of the end product (Bevilacqua and Braglia, 2000;Mobley, 2002), and this percentage is rapidly increasing (Zio and Compare, 2013). Hence, there is great incentive for asset owners to optimize the maintenance planning.The most common maintenance practices are the so-called corrective maintenance and the planned maintenance. The former as the name suggests proposes the repair of the asset upon failure, while the latter proposes a fixed service schedule for the field service engineers with the objective...

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“…Preventive maintenance can timely detect and remove pending failures and plays a key role in improving system reliability and reducing operational cost, eg, Peng et al and Pandey et al However, preventive maintenance actions require the shutdown of systems, causing expensive downtime costs. On the other hand, during system operating process, unscheduled shutdown may arise due to unavoidable external factors such as harsh weather or insufficient raw materials, which offers extra opportunities to perform maintenance actions, eg, Nakagawa et al, Zhu et al, Babishin and Taghipour, Zhu et al, and Pandey et al A number of opportunistic maintenance models have been developed by sufficiently utilizing the unavoidable shutdown.…”

Section: Introductionmentioning

confidence: 99%

“…Using the property of integral, the joint conditional probability in Equation 27 can be further derived as…”

mentioning

confidence: 99%

Reliability evaluation and opportunistic maintenance policy based on a novel delay time model

Zhang

2018

Quality & Reliability Eng

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The delay time concept is widely adopted in literature to model the two‐stage failure process of most industrial systems which can be divided into normal stage (from new to an initial point of a defect) and defective stage (from defect arrival point to failure). Most existing delay time models assume that the normal and defective stages are independent. A generalized delay time model is proposed in this paper by considering the dependence between the normal and defective stages which is reflected in the fact that they share the same external shock process. According to the definition of shot‐noise process, external shocks will incur random hazard rate increments in the two stages. The failure state is self‐announcing, whereas the defective state can only be detected by block‐based inspection or opportunistic inspection offered by unexpected shutdown due to unavoidable external factors. The system is correctively replaced upon the occurrence of a system failure or preventively replaced at the detection of a defective state. Based on the stochastic failure model and maintenance policy, this paper evaluates system reliability performance and average long‐run cost rate via a Markov‐chain based approach. Finally, a case study on a steel convertor plant is given to demonstrate the applicability of the proposed model.

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A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

Cited by 31 publications

References 38 publications

Design of multi-component periodic maintenance programs with single-component models

Design of multi-component periodic maintenance programs with single-component models

Condition-based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities

Reliability evaluation and opportunistic maintenance policy based on a novel delay time model

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