Selective maintenance optimization for multi-state systems considering stochastically dependent components and stochastic imperfect maintenance actions

Shahraki, Ameneh Forouzandeh; Yadav, Om Prakash; Vogiatzis, Chrysafis

doi:10.1016/j.ress.2019.106738

Cited by 82 publications

(27 citation statements)

References 33 publications

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“…In fact, it is difficult to precisely specify the quality of maintenance in a real environment. 27 Therefore, Shahraki et al 14 . and Chen et al 23 established the selective maintenance model of multi-state systems with maintenance quality uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Considering that there are various dependencies (economic dependence, structural dependence and stochastic dependence) between components, some researchers have studied the selective maintenance of multi-state systems with dependencies. [14][15][16][17][18][19] There are also some scholars who considered the imperfect maintenance and established the selective maintenance model for multi-state systems. Taking imperfect maintenance into consideration, some scholars established a deterministic costmaintenance quality relationship, and developed a selective maintenance of MPSS.…”

Section: Introductionmentioning

confidence: 99%

“…[20][21][22] In their model, once the relevant maintenance parameters are determined, the quality of the component after maintenance is deterministic. Shahraki et al 14 studied selective maintenance optimization for multi-state systems with stochastically dependent components and stochastic imperfect maintenance actions. Of course, some scholars have done some other studies on multi-state selective maintenance problems, [23][24][25][26] which will not be introduced in detail here.…”

Section: Introductionmentioning

confidence: 99%

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Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Ran

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In order to improve the lifetime and reliability of equipment operation stage, it is necessary to carry out maintenance for components. The typical selective maintenance model does not consider the maintenance quality uncertainty and the failure effects, which may make the decision-maker overestimates the reliability of completing the next task after system maintenance, resulting in incorrect maintenance decision and prone to serious consequences of failure. In this paper, the potential discrepancy between the target maintenance quality and the actual maintenance quality of the decision maker is considered. At the same time, a multi-state FMECA is proposed to measure the failure effects under different states. Finally, the selective maintenance model of the multi-state series systems is established and solved by the genetic algorithm. The results show that considering the effect of component failure and the uncertainty of maintenance quality has an influence on maintenance decision. The maintenance decision made in this way is more consistent with the engineering practice.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Ran

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The dependency of type (i) exists when a high setup cost required to perform maintenance, and hence it will save cost if the maintenance action is done for a group of components ([ 31 , 32 ]). The dependency of type (ii) occurs if the degradation of one component is affected by the degradation of another component ([ 33 , 34 ]). The dependency of type (iii) takes place if some components need to be maintained simultaneously [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Condition-based maintenance policy for a leased reman product

Husniah

Pasaribu

Wangsaputra

et al. 2021

Heliyon

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Many firms prefer to lease rather than to buy a product as leasing does not require a large investment cost. Leased products can be brand new products or remanufactured products (henceforth referred to as reman products). The market of reman products has grown in the last two decades due to the increasing concern of sustainability issues. This in turn brings a positive impact on the demand of leased reman products. In general, the reliability of the reman product is closed to the reliability level of a new product. To guarantee a high performance of a leased reman product, a more effective maintenance strategy is required. In this paper, we investigate a condition-based maintenance (CBM) policy to be used for maintaining a lease reman product. With the CBM policy, the condition of the reman product is monitored and controlled periodically, and hence it can avoid failure before it occurs and reduce unnecessary maintenance actions. This in turn improves the performance of the leased reman product and provides more value to the lessee. The lessor will incur a penalty cost if the performance is below a predefined threshold value. We obtain the optimal inspection interval minimizing the expected total cost and provide the numerical example for illustrating the optimal solution.

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“…Akinfiresoye and Agbetoye (2013) evaluated the usage and maintenance of farm machinery. Planning and optimizing the maintenance is necessary for such complex systems (Shahraki et al , 2020). In modern agriculture, the optimization of farm machinery management finds a great importance to save crops and reduce post-harvest losses, mainly due to unfavorable weather conditions or other uncertainty factors in the field (Pervin et al , 2020; Midya and Roy, 2014; Mahapatra et al , 2013).…”

Section: Introductionmentioning

confidence: 99%

Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability

Afsharnia¹,

Marzban²,

Asoodar³

et al. 2020

IJQRM

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PurposeThe purpose of this paper is to optimize the preventive maintenance based on fault tree (FT)–Bayesian network (BN) reliability for sugarcane harvester machine as a fundamental machine in the sugar industry that must be operated failure-free during a given period of the harvesting process.Design/methodology/approachTo determine machine reliability using the algorithm developed based on mapping FTs into BNs, the common failures of 168 machines were carefully investigated over 12 years (2007–2019). This algorithm was then used to predict the harvester reliability, estimate delays by machine downtimes and their consequences on white sugar production losses that can be reduced by optimizing the preventive maintenance scheduling.FindingsThe optimization of preventive maintenance scheduling based on estimated reliability of sugarcane harvester machines using FT–BNs can reduce white sugar production losses, the operation-stopping breakdowns and the downtime costs as a crisis that the sugar industry is facing.Practical implicationsMachine reliability gradually decreased by 31.08% approximately, which resulted in a working time loss of 26% in the 2018–19 harvesting season. In total, the white sugar losses were estimated as 204.17 tons for burnt canes and 114.53 tons for green canes. The losses of the 2018–19 harvesting season have been 11.85 times greater than the first harvesting season. The proposed maintenance interval for critical subsystems including the hydraulic, chopper and base cutter were obtained as 1.815, 1.12 and 1.05 h, respectively.Originality/valueIn this study, a new approach was used to optimize preventive maintenance to reduce delays and their implications upon costs in time, inconvenience and white sugar losses. The FT–BNs algorithm was found a useful tool that was over-fitting of failure occurrence probabilities data for sugarcane harvester machine.

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Selective maintenance optimization for multi-state systems considering stochastically dependent components and stochastic imperfect maintenance actions

Cited by 82 publications

References 33 publications

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Condition-based maintenance policy for a leased reman product

Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability

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