IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v111y2013icp1-8.html
   My bibliography  Save this article

A MC-PSO approach to the failure probability evaluation of risky plant components: The maintenance design

Author

Listed:
  • Marseguerra, M.

Abstract

A mandatory feature of the risky plants is the high reliability obtained through a careful engineering design joined with a well planned maintenance program. This latter aims at decreasing the wear thus rejuvenating the component age as if it had operated for a shorter time. The variable appearing in the failure distribution which rules the failures is then switched from chronological time to age and the (few) observed failures always occur during the early age of the component, i.e. from the lower tail of the true unknown distribution. Correspondingly, a first guess distribution based on the observed failures is strongly biased towards lower ages. In the present paper, we firstly consider the problem of estimating the true failure probability and then we apply the results to a maintenance design. In order to recover the true distribution, we propound resorting to the Particle Swarm Optimization (PSO) technique joined with a Monte Carlo (MC) simulation. We assume that the failures obey a Weibull distribution and that a set of real data has been observed from which a reference distribution has been guessed. In the PSO approach a set of agents move within the space of the two Weibull parameters: in correspondence of each location of each agent the corresponding distribution is computed and a sequence of failures is Monte Carlo sampled. The distribution thereby estimated is compared with the reference one until a match is found. The true distribution is then that corresponding to the coordinates of the agent which realized the match. Knowledge of the true failure distribution allows us to optimize the maintenance design and also to correctly compute quantities of interest involving the plant economy and the safety.

Suggested Citation

  • Marseguerra, M., 2013. "A MC-PSO approach to the failure probability evaluation of risky plant components: The maintenance design," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 1-8.
    • Handle: RePEc:eee:reensy:v:111:y:2013:i:c:p:1-8
    DOI: 10.1016/j.ress.2012.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832012001895
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2012.09.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Tieling & Xie, Min, 2011. "On the upper truncated Weibull distribution and its reliability implications," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 194-200.
    2. Hongzhou Wang & Hoang Pham, 2006. "Reliability and Optimal Maintenance," Springer Series in Reliability Engineering, Springer, number 978-1-84628-325-3, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Acitas, Sukru & Aladag, Cagdas Hakan & Senoglu, Birdal, 2019. "A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: An application to the strengths of glass fibre data," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 116-127.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    2. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    3. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    4. Ji Hwan Cha & Maxim Finkelstein, 2020. "On optimal life extension for degrading systems," Journal of Risk and Reliability, , vol. 234(3), pages 487-495, June.
    5. Maxim Finkelstein & Ji Hwan Cha, 2022. "Reducing degradation and age of items in imperfect repair modeling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1058-1081, December.
    6. Chengye Ma & Yongjun Du & Lijun Shang & Li Yang & Kaiye Gao, 2023. "Random Maintenance Strategy Modeling of Warranted Products with Reliability Heterogeneity," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    7. Panagiotis H. Tsarouhas & George K. Fourlas, 2016. "Mission reliability estimation of mobile robot system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(2), pages 220-228, June.
    8. C. Satheesh Kumar & Subha R. Nair, 2021. "A generalization to the log-inverse Weibull distribution and its applications in cancer research," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-30, December.
    9. Nguyen, Kim-Anh & Do, Phuc & Grall, Antoine, 2017. "Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 249-261.
    10. Young Yun, Won & Nakagawa, Toshio, 2010. "Replacement and inspection policies for products with random life cycle," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 161-165.
    11. E. Lerzan Örmeci & Evrim Didem Güneş & Derya Kunduzcu, 2016. "A Modeling Framework for Control of Preventive Services," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 227-244, May.
    12. Jiang, R. & Murthy, D.N.P., 2011. "A study of Weibull shape parameter: Properties and significance," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1619-1626.
    13. Sidibe, I.B. & Khatab, A. & Diallo, C. & Kassambara, A., 2017. "Preventive maintenance optimization for a stochastically degrading system with a random initial age," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 255-263.
    14. Esposito, Nicola & Mele, Agostino & Castanier, Bruno & GIORGIO, Massimiliano, 2023. "A hybrid maintenance policy for a deteriorating unit in the presence of three forms of variability," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    15. Wang, Wenbin, 2011. "An inspection model based on a three-stage failure process," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 838-848.
    16. Wang, Wenbin, 2012. "An overview of the recent advances in delay-time-based maintenance modelling," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 165-178.
    17. Hamidi, Maryam & Szidarovszky, Ferenc & Szidarovszky, Miklos, 2016. "New one cycle criteria for optimizing preventive replacement policies," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 42-48.
    18. Ahtasham Gul & Muhammad Mohsin & Muhammad Adil & Mansoor Ali, 2021. "A modified truncated distribution for modeling the heavy tail, engineering and environmental sciences data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-24, April.
    19. Retsef Levi & Thomas Magnanti & Jack Muckstadt & Danny Segev & Eric Zarybnisky, 2014. "Maintenance scheduling for modular systems: Modeling and algorithms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(6), pages 472-488, September.
    20. Ahmad, Abd EL-Baset A. & Ghazal, M.G.M., 2020. "Exponentiated additive Weibull distribution," Reliability Engineering and System Safety, Elsevier, vol. 193(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:111:y:2013:i:c:p:1-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.