Joint redundancy and imperfect preventive maintenance optimization for series–parallel multi-state degraded systems

Nourelfath, Mustapha; Châtelet, Éric; Nahas, Nabil

doi:10.1016/j.ress.2012.03.004

Cited by 69 publications

(36 citation statements)

References 37 publications

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“…The key idea behind it is that, even though the repaired element is restored to its best state after repair, it cannot be considered to be in a completely new condition, and the degradation trend accelerates in the next repair cycle. This important phenomena has been extensively observed in many engineered systems [38], but cannot be characterized by existing imperfect maintenance models of MSSs [31], [34], [35], [37]. As an extension of our previous work, a RAP incorporated with element replacement strategy optimization under imperfect repair is formulated in this paper.…”

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confidence: 94%

“…Some possible errors and misunderstanding associated with their work have been discussed in [36]. To quantify the efficiency of imperfect preventive maintenance for MSEs, Nourelfath et al [37] put forth a Markov model for preventively maintained MSEs. The MSE which has degraded to its unacceptable state will restore to a better state after a preventive maintenance.…”

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confidence: 99%

“…The more repair resources spent, the better condition the repaired element will restore to. Another contribution of this work compared with the existing literature [31], [32], [37] lies in the introduction of two hypothetical functional relations which enable us to treat the imperfect repair efficiency as a function of the assigned repair expenditure. This idea is highly desired in practical maintenance management because it provides much more flexibility to the maintenance decision makers.…”

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confidence: 99%

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A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems

et al. 2013

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Abstract-The redundancy allocation problem has been extensively studied with the aim of determining optimal redundancy levels of components at various stages to achieve the required system reliability or availability. In most existing studies, failed elements are assumed to be as good as new after repair, from a failure perspective. Due to deterioration, the repaired element cannot always be restored to a virtually new condition unless replaced with a new element. In this paper, we present an approach of joint redundancy and imperfect maintenance strategy optimization for multi-state systems. Along with determining the optimal redundancy levels, the element replacement strategy under imperfect repair is also optimized simultaneously, so as to reach the desired availability with minimal average expenditure. A generalized imperfect repair model is proposed to characterize the stochastic behavior of multi-state elements (MSEs) after repair, and a replacement policy under which a MSE is replaced once it reaches the pre-determined number of failures is introduced. The cost-repair efficiency relation, which regards the imperfect repair efficiency as a function of assigned repair cost, is put forth to provide a flexibility of assigning repair efforts strategically among MSEs. The benefits of the proposed method compared to the existing ones are demonstrated and verified via an illustrative case study of a three-stage coal transportation system.

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confidence: 94%

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A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems

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“…Soro et al [27] suggested that if the system reaches the last acceptable degraded state, it is brought back to one of the states with higher efciency employing preventive maintenance. Nourelfath et al [28] formulated a joint redundancy and imperfect preventive maintenance planning optimization model for series-parallel multi-state degraded systems.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Multi-State Degraded System to Optimize Maintenance/Repair Costs and System Availability

et al. 2017

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Abstract. In this study, a multi-state degraded system is studied, where status of system is continuously degrading over time. As time progresses, system may either deteriorate gradually and go to lower performance state, or it may fail suddenly. If the system fails, some repairs are carried out to restore the system to the previous state. When the inspections reveal that the system has reached its last acceptable state, a PM is carried out to restore the system to the higher performance states. The goal is to nd the optimal PM level, so that the mean availability of the system is maximized and the total cost of the system is minimized. In this regard, Markov process is employed to represent di erent states of system. An integrated optimization approach is also suggested based on the desirability function of statistical approach. The suggested aggregation method is robust to the potential dependency between the total cost and the mean availability. It also ensures that both objective functions fall in decision-maker's acceptable region. In order to show the e ciency of the proposed approach, a numerical example is presented and analyzed.

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“…But this method need the simulation calculation on each discrete time point and large amount of calculation. So the literatures [4][5][6][7][8] put forward the universal generating function method. The method discreted the input parameter and combined the discrete state of the input parameters.…”

Section: Introductionmentioning

confidence: 99%

Estimating reliability of degraded system based on the probability density evolution with multi-parameter

2017

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System degradation was usually caused by multiple-parameter degradation. The assessment result of system reliability by universal generating function was low accurate when compared with the Monte Carlo simulation. And the probability density function of the system output performance cannot be got. So the reliability assessment method based on the probability density evolution with multi-parameter was presented for complexly degraded system. Firstly, the system output function was founded according to the transitive relation between component parameters and the system output performance. Then, the probability density evolution equation based on the probability conservation principle and the system output function was established. Furthermore, probability distribution characteristics of the system output performance was obtained by solving differential equation. Finally, the reliability of the degraded system was estimated. This method did not need to discrete the performance parameters and can establish continuous probability density function of the system output performance with high calculation efficiency and low cost. Numerical example shows that this method is applicable to evaluate the reliability of multi-parameter degraded system.

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Joint redundancy and imperfect preventive maintenance optimization for series–parallel multi-state degraded systems

Cited by 69 publications

References 37 publications

A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems

A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems

A Multi-Objective Multi-State Degraded System to Optimize Maintenance/Repair Costs and System Availability

Estimating reliability of degraded system based on the probability density evolution with multi-parameter

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