Optimal inspection period and replacement policy for CBM with imperfect information using PHM

Ghasemi, Alireza; Yacout, Soumaya; Ouali, M-Salah; Ao, Sio-Iong; Amouzegar, Mahyar A.; Chen, Su-Shing

doi:10.1063/1.2937611

Cited by 8 publications

(7 citation statements)

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“…It is used with one dimensional signal as well as multidimensional ones (Ye et al 2002). The normal and the faulty states are considered hidden by Bunks et al (2000), Ying et al (2000) and Ghasemi et al (2007Ghasemi et al ( , 2008. Applications of this model are given by Ge et al (2004) and Li et al (2004).…”

Section: The Hidden Markov Modelmentioning

confidence: 98%

Accuracy and robustness of decision making techniques in condition based maintenance

2009

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This paper presents a study on Type I and Type II errors that are inherent in every technique for decision making in condition based maintenance. Specifically, the study considers those errors that result when three methods are used for decision making: the control charts known as the Statistical Process Control, the Hidden Markov Model, and the Proportional Hazard Model. The objective is to study the accuracy and the robustness of these techniques to variations in the models' parameters. Monte Carlo simulation is used to simulate the performance of these techniques. The effects of parameter's variations are obtained through Taguchi's design of experiments, and the results are analyzed by the analysis of variance technique. Those results show that accuracy and robustness should be taken into consideration when maintenance decisions are taken. Optimization techniques can be used to optimize the values of the parameters in order to minimize the two types of errors and to increase the correct decisions.

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Section: The Hidden Markov Modelmentioning

confidence: 98%

Accuracy and robustness of decision making techniques in condition based maintenance

2009

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“…Remark The inspection models discussed in the latter three papers are clearly a departure from the classical inspection models such as the ones in the works of some authors and many others mentioned in surveys such as the one by Beichelt and Tittmann in the sense that the objective here is not to recommend times on when inspections should take place but rather to set out an order or hierarchy in which the components of a system may be inspected in the event of a system failure.…”

Section: Introductionmentioning

confidence: 99%

An ideal way of obtaining an optimal inspection permutation for a system with components connected in series

Chipoyera

2016

Appl Stoch Models Bus & Ind

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The problem of an inspection permutation or inspection strategy (first discussed in a research paper in 1989 and reviewed in another research paper in 1991) is revisited. The problem deals with an N‐component system whose times to failure are independent but not identically distributed random variables. Each of the failure times follows an exponential distribution. The components in the system are connected in series such that the failure of at least one component entails the failure of the system. Upon system failure, the components are inspected one after another in a hierarchical way (called an inspection permutation) until the component causing the system to fail is identified. The inspection of each component is a process that takes a non‐negligible amount of time and is performed at a cost. Once the faulty component is identified, it is repaired at a cost, and the repair process takes some time. After the repair, the system is good as new and is put back in operation. The inspection permutation that results in the maximum long run average net income per unit of time (for the undiscounted case) or maximum total discounted net income per unit of time (for the discounted case) is called the optimal inspection permutation/strategy. A way of determining an optimal inspection permutation in an easier fashion, taking advantage of the improvements in computer software, is proffered. Mathematica is used to showcase how the method works with the aid of a numerical example. Copyright © 2016 John Wiley & Sons, Ltd.

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“…It is assumed that alternative inspection intervals are integer multiples of a so-called basic inspection interval. In Ghasemi et al (2008), the assumption of non-costly inspection in Ghasemi et al (2007) is relaxed and the optimal replacement threshold and the optimal inspection interval are determined by evaluating different inspection intervals, with the same assumption that the inspections do not reveal the exact degradation state of the system and that the corresponding degradation state transition matrix for different possible intervals are known. It is notable that in both above-mentioned research works it is assumed that the time between two consecutive inspections is constant.…”

Section: Introductionmentioning

confidence: 99%

Optimal replacement policy for condition-based maintenance with non-decreasing failure cost and costly inspection

Golmakani

Pouresmaeeli

2014

Journal of Quality in Maintenance Engineering

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Purpose – The purpose of this paper is to determine optimal replacement threshold and optimal inspection interval for an item subjected to condition-based maintenance (CBM). The primarily assumption is that the item's failure replacement cost depends on the item's degradation state at which failure occurs and/or the time the item fails. The cost of inspection is also taken into account. Design/methodology/approach – The control limit replacement policy framework, already reported by some research referred to in this paper, is first extended to include the non-decreasing failure replacement cost assumption. Then, for alternative inspection intervals, replacement thresholds together with their associated total cost including the inspection cost are computed. By comparing the total costs, the optimal inspection interval and its corresponding optimal replacement threshold are simultaneously identified. Findings – The mathematical formulation required for the determination of optimal replacement threshold and optimal inspection interval for an item subjected to CBM under the assumption of non-decreasing failure cost is provided. Practical implications – In some practical situations where CBM is implemented, the failure replacement cost may depend on the time the failure happens and/or may depend on the system's degradation state. In addition, inspections often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection interval for the underlying system. Originality/value – Using the approach proposed in this paper, one could obtain the optimal replacement threshold and the optimal inspection interval for a system subjected to CBM.

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Optimal inspection period and replacement policy for CBM with imperfect information using PHM

Cited by 8 publications

References 0 publications

Accuracy and robustness of decision making techniques in condition based maintenance

Accuracy and robustness of decision making techniques in condition based maintenance

An ideal way of obtaining an optimal inspection permutation for a system with components connected in series

Optimal replacement policy for condition-based maintenance with non-decreasing failure cost and costly inspection

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