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Optimization of alarm threshold and sequential inspection scheme

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  • Jiang, R.

Abstract

An item is subject to gradual degradation. The degradation can be represented by a measurable, non-negative and non-decreasing quantity. The item can be in one of three different states: normal (when the degradation quantity is smaller than a threshold of alarm or potential failure), functional failure (when the degradation quantity is larger than a functional failure threshold) and in-between or potential failure (when the degradation quantity is larger than the alarm threshold and smaller than the functional failure threshold). A sequential inspection scheme is implemented to determine the state of the item so as to prevent a functional failure. The paper presents a flexible degradation model and two cost models to optimize the alarm threshold and the sequential inspection scheme. The usefulness and appropriateness of the proposed models are illustrated by examples.

Suggested Citation

  • Jiang, R., 2010. "Optimization of alarm threshold and sequential inspection scheme," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 208-215.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:3:p:208-215
    DOI: 10.1016/j.ress.2009.09.012
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    References listed on IDEAS

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    1. Jiang, R. & Jardine, A.K.S., 2006. "Composite scale modeling in the presence of censored data," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 756-764.
    2. Jiang, R. & Jardine, A.K.S., 2008. "Health state evaluation of an item: A general framework and graphical representation," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 89-99.
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    4. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    5. X Jia & A H Christer, 2002. "A prototype cost model of functional check decisions in reliability-centred maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(12), pages 1380-1384, December.
    6. Yuan, X.-X. & Pandey, M.D., 2009. "A nonlinear mixed-effects model for degradation data obtained from in-service inspections," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 509-519.
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    Citations

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    Cited by:

    1. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    2. Zou, Guang & Faber, Michael Havbro & González, Arturo & Banisoleiman, Kian, 2021. "Computing the value of information from periodic testing in holistic decision making under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    3. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    4. Hui-Ying Wang & Zhao-Qiang Wang, 2022. "A condition-based preventive replacement policy with imperfect manual inspection for a two-stage deterioration process," Journal of Risk and Reliability, , vol. 236(2), pages 225-236, April.
    5. Havinga, Maik J.A. & de Jonge, Bram, 2020. "Condition-based maintenance in the cyclic patrolling repairman problem," International Journal of Production Economics, Elsevier, vol. 222(C).
    6. Jiang, R., 2013. "A multivariate CBM model with a random and time-dependent failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 178-185.
    7. Gössinger, Ralf & Helmke, Hanna & Kaluzny, Michael, 2017. "Condition-based release of maintenance jobs in a decentralised production-maintenance system – An analysis of alternative stochastic approaches," International Journal of Production Economics, Elsevier, vol. 193(C), pages 528-537.
    8. Wang, Wenbin & Zhao, Fei & Peng, Rui, 2014. "A preventive maintenance model with a two-level inspection policy based on a three-stage failure process," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 207-220.
    9. Jiang, R., 2018. "Performance evaluation of seven optimization models of age replacement policy," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 302-311.
    10. Michael Jong Kim & Viliam Makis, 2013. "Joint Optimization of Sampling and Control of Partially Observable Failing Systems," Operations Research, INFORMS, vol. 61(3), pages 777-790, June.

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