IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v218y2022ipbs0951832021006529.html
   My bibliography  Save this article

Degradation and reliability of multi-function systems using the hazard rate matrix and Markovian approximation

Author

Listed:
  • Zhou, Daoqing
  • Sun, C.P.
  • Du, Yi-Mu
  • Guan, Xuefei

Abstract

The study presents a new method to model the degradation and reliability of multi-function complex systems using the hazard rate matrix and Markovian approximation. The system is hierarchically decomposed into a set of states according to the states of its functions, and the hazard rate matrix is proposed to describe the failure rates of the functions. By using Markovian approximation, the elements of the hazard rate matrix can be expressed as a set of dynamical equations involving the failure information about the functions. Consequently, the dynamical behavior of the degradation for the whole system and its functions can be determined using the observed failure data of those functions instead of the lifetime data of the whole system, eliminating the need for lifetime testing data of the whole system in statistical-based reliability assessment. The overall method is illustrated using an example problem, and is further applied to a power system degradation problem to demonstrate its usage in realistic engineering applications.

Suggested Citation

  • Zhou, Daoqing & Sun, C.P. & Du, Yi-Mu & Guan, Xuefei, 2022. "Degradation and reliability of multi-function systems using the hazard rate matrix and Markovian approximation," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
  • Handle: RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006529
    DOI: 10.1016/j.ress.2021.108166
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021006529
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2021.108166?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. He, Jingjing & Huang, Min & Wang, Wei & Wang, Shaohua & Guan, Xuefei, 2021. "An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Jinhua Mi & Yan-Feng Li & Weiwen Peng & Hong-Zhong Huang, 2018. "Reliability Analysis of Complex Multi-state System with Common Cause Failure Based on DS Evidence Theory and Bayesian Network," Springer Series in Reliability Engineering, in: Anatoly Lisnianski & Ilia Frenkel & Alex Karagrigoriou (ed.), Recent Advances in Multi-state Systems Reliability, pages 19-38, Springer.
    3. Lisnianski, Anatoly, 2007. "Extended block diagram method for a multi-state system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1601-1607.
    4. Zhou, Daoqing & He, Jingjing & Du, Yi-Mu & Sun, C.P. & Guan, Xuefei, 2021. "Probabilistic information fusion with point, moment and interval data in reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    5. Volkanovski, Andrija & ÄŒepin, Marko & Mavko, Borut, 2009. "Application of the fault tree analysis for assessment of power system reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1116-1127.
    6. Choe, Do-Eun & Gardoni, Paolo & Rosowsky, David & Haukaas, Terje, 2008. "Probabilistic capacity models and seismic fragility estimates for RC columns subject to corrosion," Reliability Engineering and System Safety, Elsevier, vol. 93(3), pages 383-393.
    7. Dongjin Lee & Rong Pan, 2017. "Predictive maintenance of complex system with multi-level reliability structure," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4785-4801, August.
    8. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    9. Nguyen, H.D. & Gouno, E., 2020. "Bayesian inference for Common cause failure rate based on causal inference with missing data," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    10. Guo, Haitao & Yang, Xianhui, 2007. "A simple reliability block diagram method for safety integrity verification," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1267-1273.
    11. Kelly, Dana L. & Smith, Curtis L., 2009. "Bayesian inference in probabilistic risk assessment—The current state of the art," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 628-643.
    12. Kang, Won-Hee & Song, Junho & Gardoni, Paolo, 2008. "Matrix-based system reliability method and applications to bridge networks," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1584-1593.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Rongxi & Li, Yufan & Xu, Jinjin & Wang, Zhen & Gao, Jianmin, 2022. "F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Leonardo Leoni & Farshad BahooToroody & Saeed Khalaj & Filippo De Carlo & Ahmad BahooToroody & Mohammad Mahdi Abaei, 2021. "Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice," IJERPH, MDPI, vol. 18(7), pages 1-16, March.
    3. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Jayaraman, Deepan & Ramu, Palaniappan, 2023. "L-moments and Bayesian inference for probabilistic risk assessment with scarce samples that include extremes," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Mi, Jinhua & Beer, Michael & Li, Yan-Feng & Broggi, Matteo & Cheng, Yuhua, 2020. "Reliability and importance analysis of uncertain system with common cause failures based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    6. Bistouni, Fathollah & Jahanshahi, Mohsen, 2014. "Analyzing the reliability of shuffle-exchange networks using reliability block diagrams," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 97-106.
    7. Morshedi, Mohamad Ali & Kashani, Hamed, 2022. "Assessment of vulnerability reduction policies: Integration of economic and cognitive models of decision-making," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Kowal, Karol, 2022. "Lifetime reliability and availability simulation for the electrical system of HTTR coupled to the electricity-hydrogen cogeneration plant," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    9. Bodda, Saran Srikanth & Gupta, Abhinav & Dinh, Nam, 2020. "Enhancement of risk informed validation framework for external hazard scenario," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Li, Xiang-Yu & Xiong, Xiaoyan & Guo, Junyu & Huang, Hong-Zhong & Li, Xiaopeng, 2022. "Reliability assessment of non-repairable multi-state phased mission systems with backup missions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    11. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Gabriel, Angelito & Ozansoy, Cagil & Shi, Juan, 2018. "Developments in SIL determination and calculation," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 148-161.
    13. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    14. Li, He & Deng, Zhi-Ming & Golilarz, Noorbakhsh Amiri & Guedes Soares, C., 2021. "Reliability analysis of the main drive system of a CNC machine tool including early failures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Byun, Ji-Eun & Zwirglmaier, Kilian & Straub, Daniel & Song, Junho, 2019. "Matrix-based Bayesian Network for efficient memory storage and flexible inference," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 533-545.
    16. Zhou, Hang & Lopes Genez, Thiago Augusto & Brintrup, Alexandra & Parlikad, Ajith Kumar, 2022. "A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    17. Akwasi F. Mensah & Leonardo Dueñas-Osorio, 2012. "A Closed-Form Technique for the Reliability and Risk Assessment of Wind Turbine Systems," Energies, MDPI, vol. 5(6), pages 1-17, June.
    18. George-Williams, Hindolo & Patelli, Edoardo, 2016. "A hybrid load flow and event driven simulation approach to multi-state system reliability evaluation," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 351-367.
    19. Andrzej Tytko & Grzegorz Olszyna & Grzegorz Kocór & Mariusz Szot, 2023. "Some Stochastic Aspects of Safety Work of Steel Wire Ropes Used in Mining-Shaft Hoists," Sustainability, MDPI, vol. 15(9), pages 1-13, May.
    20. Hui Xiao & Minhao Cao & Gang Kou & Xiaojun Yuan, 2021. "Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing," Journal of Risk and Reliability, , vol. 235(2), pages 282-292, April.

    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:218:y:2022:i:pb:s0951832021006529. 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.