An extended optimal replacement model for a deteriorating system with inspections

Sheu, Shey‐Huei; Tsai, Hsin-Nan; Wang, Fu‐Kwun; Zhang, Zhe George

doi:10.1016/j.ress.2015.01.014

Cited by 21 publications

(11 citation statements)

References 35 publications

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“…The rules for decommissioning the system (given in Assumptions 4 and 7) are very similar to the replacement rules in the papers by Zuckerman [44] and Sheu et al [31] as well as the conditions for retiring the system in Berrade et al [19]. Rangan's and Grace's [45] 'Replacement Policy T' rules for replacing a system are somewhat similar to the replacement rules used in this paper.…”

Section: Remarkmentioning

confidence: 68%

“…Roeloffs , Kander and Raviv , and also Beichelt separately looked at the case where T 's distribution is partially known (only one percentile of T being known). Like in most papers published (e.g., Barlow et al , Luss and Kander , Kabir and El Tamimi , and more recent papers such as Wang , Ahmadi and Newby , Wang , Caballé et al , and Sheu et al ), in this paper, we assume that T 's distribution is completely known. The assumptions of the model are as follows: When working, the system generates or brings in revenue at a constant rate of c R per unit of time. The time it takes to complete each and every inspection is negligible, and each inspection costs an amount of c I – this is a common assumption in most papers on inspection replacement models that have been published .…”

Section: A Simple Finite Planning Horizon Inspection Modelmentioning

confidence: 99%

“…Roeloffs [39], Kander and Raviv [40], and also Beichelt [38] separately looked at the case where T's distribution is partially known (only one percentile of T being known). Like in most papers published (e.g., Barlow et al [1], Luss and Kander [2], Kabir and El Tamimi [33], and more recent papers such as Wang [9], Ahmadi and Newby [26], Wang [41], Caballé et al [14], and Sheu et al [31]), in this paper, we assume that T's distribution is completely known. The assumptions of the model are as follows:…”

Section: The Modelmentioning

confidence: 99%

See 2 more Smart Citations

Optimal scheduling of inspection times in a production process with a finite planning horizon

Chipoyera

2016

Appl Stoch Models Bus & Ind

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Inspection models applicable to a finite planning horizon are developed for the following lifetime distributions: uniform, exponential, and Weibull distribution. For a given lifetime distribution, maximization of profit is used as the sole optimization criterion for determining an optimal planning horizon over which a system may be operated as well as ideal inspection times. Illustrative examples (focusing on the uniform and Weibull distributions and using Mathematica programs) are given. For some situations, evenly spreading inspections over the entire planning horizon are seen to result in the attainment of desirable profit levels over a shorter planning horizon. Scope for further research is given as well. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Remarkmentioning

confidence: 68%

Section: A Simple Finite Planning Horizon Inspection Modelmentioning

confidence: 99%

Section: The Modelmentioning

confidence: 99%

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Optimal scheduling of inspection times in a production process with a finite planning horizon

Chipoyera

2016

Appl Stoch Models Bus & Ind

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“…When a failure is triggered for component i , i = 1 , 2 , ⋯ , m , it is a catastrophic type with probability q i ( t ) and minor type with probability

{\overset{true¯}{q}}_{i} ((), t)

, where

{\overset{true¯}{q}}_{i} ((), t) = 1 - q_{i} ((), t)

. q i ( t ) is assumed to be a non‐decreasing function (see, eg, Sheu et al). Note that, failure can only happen once for each component.…”

Section: Problem Descriptionmentioning

confidence: 99%

An inspection model for a multi‐component system subject to 2 types of failures

Yang

Zhao

2017

Quality & Reliability Eng

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Group maintenance is common and of significant importance for complex systems in industrial applications. This paper proposes a novel inspection and replacement model for a multi‐component system whose components are all subject to 2 typical failure modes, ie, catastrophic failure and minor failure. A catastrophic failure stops the system immediately, whereas a minor failure is not fatal and could only be identified by periodic inspection. At either a catastrophic or a minor failure, replacement is immediate. The maintenance cost model could be constructed through calculating the distribution of the “forward time”, which denotes the time elapse to a catastrophic failure since the previous inspection. The objective of this paper is to minimize the expected cost per unit time of the system via the optimization of the inspection interval. A case study on offshore wind turbine blades is presented to illustrate the maintenance model.

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“…Zhang (1994) generalized Lam's work by a bivariate replacement policy (T, N) under which the system is replaced at the working age T or at the time of the Nth failure, whichever occurs first, and showed that the bivariate optimal policy (T, N) * is better than the univariate optimal policies N * and T * under some conditions. Many replacement problems have been done by Finkelstein (1993), Stanley (1993), Sheu (1999), Zhang et al (2002Zhang et al ( , 2007, Zhang (2004), Wang and Zhang (2006, Chen and Li (2008), Zhang and Wang (2011) and others on the geometric process repair model. In all the above research works for the simple repairable system, a common assumption is that the system will be repaired as soon as it fails.…”

Section: Introductionmentioning

confidence: 99%

An optimal age-replacement policy for a simple repairable system with delayed repair

Zhang

Wang

2016

Communications in Statistics - Theory and Methods

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In this article, a simple repairable system (i.e. a repairable system consisting of one component and one repairman) with delayed repair is studied. Assume that the system after repair is not "as good as new", and the degeneration of the system is stochastic. Under these assumptions, using the geometric process repair model, we consider a replacement policy T based on system age under which the system is replaced when the system age reaches T . Our problem is to determine an optimal replacement policy T * , such that the average cost rate (i.e. the long-run average cost per unit time) of the system is minimized. The explicit expression of the average cost rate is derived, the corresponding optimal replacement policy T * can be determined by minimizing the average cost rate of the system. Finally, a numerical example is given to illustrate some theoretical results and the model's applicability.

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An extended optimal replacement model for a deteriorating system with inspections

Cited by 21 publications

References 35 publications

Optimal scheduling of inspection times in a production process with a finite planning horizon

Optimal scheduling of inspection times in a production process with a finite planning horizon

An inspection model for a multi‐component system subject to 2 types of failures

An optimal age-replacement policy for a simple repairable system with delayed repair

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