Opportunistic perfect preventive maintenance policy in presence of masked data

Misaii, Hasan; Haghighi, Firoozeh; Fouladirad, Mitra

doi:10.1177/1748006x211058936

Cited by 5 publications

(8 citation statements)

References 29 publications

(45 reference statements)

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“…Scenario (14). Defect in ½(j À 1)s, js, failure in ½(i À 2)s, (i À 1)s, default at Ws, no opportunities and corrective replacement at is (j = K + 1, :::, i À 2, i = W + 1, :::, M, W = K, :::, M À 1, K = 0, :::, M À 3):…”

Section: Data Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In a multi-component system, corrective action (in the event of failure) or preventive action on one component may provide an opportunity to maintain others. 13,14 In such cases, components may be stochastically dependent 15 (wear or failure dependence) and maintenance may be imperfect. 16 Plant may be stopped due to material shortage, harsh environmental conditions or low market prices.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid maintenance policy with fixed periodic structure and opportunistic replacement

Melo¹,

Cavalcante²,

Scarf

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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We model a maintenance policy with fixed periodic structure that is a hybrid of periodic inspection and opportunistic replacement. The policy is applicable to geographically remote systems such as offshore wind farms. The policy has three phases. Initially, there is an inspection phase to identify early defects. This is followed by a wear out phase during which corrective replacements are performed. Preventive replacement occurs at the end of this phase. The novelty of the model is an opportunistic phase, which overlaps with the latter part of the corrective phase, when preventive replacement is executed early if an opportunity arises. In this way, we model the reality in which remote systems with high logistics costs and restricted access may benefit from opportunistic visits for maintenance. Using a numerical example, we analyse the behaviour of the decision variables for a range of values of the parameters common to such systems. These parameters relate to: component heterogeneity; restricted access; default (failure to execute a planned action); arrival of opportunities and other standard parameters in a maintenance cost model. Specifically, our results indicate when opportunities can have a significant impact on the cost-rate of the optimum policy, but that leveraging opportunities cannot achieve a very high availability. Generally, we demonstrate that maintenance planning should be flexible when factors beyond the control of the maintainer impact maintenance effectiveness.

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Section: Data Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid maintenance policy with fixed periodic structure and opportunistic replacement

Melo¹,

Cavalcante²,

Scarf

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…Few works has been reported on maintenance modeling of systems with masked failures. To the best of our knowledge, Misaii et al 13 . may be the only study on maintenance optimization considering masked failure.…”

Section: Introductionmentioning

confidence: 99%

“…Few works has been reported on maintenance modeling of systems with masked failures. To the best of our knowledge, Misaii et al 13 may be the only study on maintenance optimization considering masked failure. In the study, maintenance actions are carried out only if the system has failed at each periodic inspection epoch.…”

Section: Introductionmentioning

confidence: 99%

Maintenance optimization of a two‐component series system considering masked causes of failure

Hu,

Huang,

Shen

2023

Quality & Reliability Eng

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Maintenance planning of two‐component systems has been extensively studied in recent decades. In the literature, most studies assume that the failure cause of a two‐component system is self‐announcing. In some real applications, the failure cause is masked, and a diagnosis with professional equipment is needed to reveal the failed component. This study investigates a preventive replacement policy of a two‐component series system considering masked causes of failure. When an unexpected failure occurs, we can carry out a diagnosis to reveal the failed component and replace it subsequently, or we can directly replace the whole system without diagnosis. Meanwhile, when we carry out a preventive replacement on a component, the other component can be replaced opportunistically. We formulate the problem as a semi‐Markov decision process, and prove the existence of the stationary optimal policy. The optimal preventive replacement age thresholds for each component and the corresponding optimal maintenance actions upon each failure are jointly obtained to minimize the long‐term average maintenance cost per time unit. A comprehensive numerical study is provided to illustrate the effectiveness of our proposed model.

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Optimal perfect corrective maintenance policy for a system with multiple components using data‐driven decision‐making methods

Misaii,

Fouladirad,

Haghighi

2023

Quality & Reliability Eng

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This paper considers a multicomponent series system to be inspected periodically. It is assumed that the exact cause of system failure might be unknown, and we only know that it belongs to a subset of all components, called masked set. In this case, it is said that the exact cause of failure is masked. A perfect corrective maintenance policy is applied to repair the system, such that a failed component is replaced by a new one at the first inspection time after failure. The distance between each two inspections is considered a decision parameter that should be optimized. Three levels of information availability are considered, including full, partial, and noninformation availability, in which the maintenance policy should be optimized. The former is considered a benchmark, and the statistical and machine learning data‐driven algorithms are used to estimate the cost for the other two information levels. Eventually, using Monte Carlo simulation studies, the proposed method application is analyzed.

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Opportunistic perfect preventive maintenance policy in presence of masked data

Cited by 5 publications

References 29 publications

A hybrid maintenance policy with fixed periodic structure and opportunistic replacement

A hybrid maintenance policy with fixed periodic structure and opportunistic replacement

Maintenance optimization of a two‐component series system considering masked causes of failure

Optimal perfect corrective maintenance policy for a system with multiple components using data‐driven decision‐making methods

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