IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v165y2017icp410-421.html
   My bibliography  Save this article

Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method

Author

Listed:
  • Byun, Ji-Eun
  • Noh, Hee-Min
  • Song, Junho

Abstract

The advent of ever more complex systems in extensive areas of industries hampers efficient and accurate analysis of reliability and effective reliability-based decision making. Such difficulties may arise from the intricate formulation of system failure events, statistical dependence between component failure events, and the convoluted quantification of underlying probabilities of basic events. So-called k-out-of-N systems, which survive or succeed when at least k components are available among the total of N components, give rise to a high level of complexity. This type of systems are commonly introduced to secure a proper level of redundancy in operating engineering systems, but the intricate definition of the system events may elude the system reliability analysis. It is noted that such k-out-of-N systems are often tested and corrected over a certain period of time before their official usage or release in order to assure the target reliability of the system. For the purpose of reliability prognosis based on the data collected from the test period, reliability growth models (RGMs) have been widely used in software and hardware engineering. However, RGMs have been applied mostly to individual components, not at the system level. Furthermore, in complex systems such as k-out-of-N system, it is challenging to relate the reliability growth of components with that of the system. To address this need, in this paper, the matrix-based system reliability (MSR) method is extended to k-out-of-N systems by modifying the formulations of event and probability vectors. The proposed methods can incorporate statistical dependence between component failures for both homogeneous and non-homogeneous k-out-of-N systems, and can compute measures related to parameter sensitivity and relative importance of components. The reliability growths of components represented by RGMs are incorporated into the proposed system reliability method, so that the trend of system reliability growth can be effortlessly evaluated and predicted. Two numerical examples are introduced in this paper to demonstrate the proposed method and its applications: (1) hypothetical systems each consisting of series, parallel and k-out-of-N subsystems, and (2) a simplified high speed train system modeled by multiple k-out-of-N subsystems. Two types of RGMs, i.e. non-homogeneous Poisson process (NHPP) and Duane models are employed in these examples.

Suggested Citation

  • Byun, Ji-Eun & Noh, Hee-Min & Song, Junho, 2017. "Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 410-421.
  • Handle: RePEc:eee:reensy:v:165:y:2017:i:c:p:410-421
    DOI: 10.1016/j.ress.2017.05.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832016306068
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2017.05.001?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. 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.
    2. Peng, R. & Li, Y.F. & Zhang, W.J. & Hu, Q.P., 2014. "Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 37-43.
    3. Der Kiureghian, Armen & Ditlevsen, Ove D. & Song, Junho, 2007. "Availability, reliability and downtime of systems with repairable components," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 231-242.
    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. Byun, Ji-Eun & de Oliveira, Welington & Royset, Johannes O., 2023. "S-BORM: Reliability-based optimization of general systems using buffered optimization and reliability method," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    2. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Eryilmaz, Serkan, 2018. "The number of failed components in a k-out-of-n system consisting of multiple types of components," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 246-250.
    4. María Luz Gámiz & Delia Montoro-Cazorla & María del Carmen Segovia-García & Rafael Pérez-Ocón, 2022. "MoMA Algorithm: A Bottom-Up Modeling Procedure for a Modular System under Environmental Conditions," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
    5. Byun, Ji-Eun & Song, Junho, 2021. "A general framework of Bayesian network for system reliability analysis using junction tree," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2019. "Reliability-based measures and prognostic analysis of a K-out-of-N system in a random environment," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1120-1131.
    7. Lyu, Dong & Si, Shubin, 2020. "Dynamic importance measure for the K-out-of-n: G system under repeated random load," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    8. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    9. Byun, Ji-Eun & Song, Junho, 2021. "Generalized matrix-based Bayesian network for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    10. He, Gang & Wu, Wenqing & Zhang, Yuanyuan, 2018. "Analysis of a multi-component system with failure dependency, N-policy and vacations," Operations Research Perspectives, Elsevier, vol. 5(C), pages 191-198.
    11. Wei Wang & Yaofeng Xu & Liguo Hou, 2019. "Optimal allocation of test times for reliability growth testing with interval-valued model parameters," Journal of Risk and Reliability, , vol. 233(5), pages 791-802, October.

    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. Qing Tian & Chun-Wu Yeh & Chih-Chiang Fang, 2022. "Bayesian Decision Making of an Imperfect Debugging Software Reliability Growth Model with Consideration of Debuggers’ Learning and Negligence Factors," Mathematics, MDPI, vol. 10(10), pages 1-21, May.
    2. Rodríguez, Joanna & Lillo, Rosa E. & Ramírez-Cobo, Pepa, 2015. "Failure modeling of an electrical N-component framework by the non-stationary Markovian arrival process," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 126-133.
    3. Yu, Juanya & Sharma, Neetesh & Gardoni, Paolo, 2024. "Functional connectivity analysis for modeling flow in infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    4. Lirong Cui & Shijia Du & Aofu Zhang, 2014. "Reliability measures for two-part partition of states for aggregated Markov repairable systems," Annals of Operations Research, Springer, vol. 212(1), pages 93-114, January.
    5. Sungsik Yoon & Young-Joo Lee & Hyung-Jo Jung, 2021. "Flow-based seismic risk assessment of a water transmission network employing probabilistic seismic hazard analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1231-1254, January.
    6. Kim, Youngsuk & Kang, Won-Hee, 2013. "Network reliability analysis of complex systems using a non-simulation-based method," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 80-88.
    7. Monfared, M.A.S. & Rezazadeh, Masoumeh & Alipour, Zohreh, 2022. "Road networks reliability estimations and optimizations: A Bi-directional bottom-up, top-down approach," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    8. Hiroyuki Okamura & Tadashi Dohi, 2016. "Phase-type software reliability model: parameter estimation algorithms with grouped data," Annals of Operations Research, Springer, vol. 244(1), pages 177-208, September.
    9. Roberto Benato & Antonio Chiarelli & Sebastian Dambone Sessa, 2021. "Reliability Assessment of a Multi-State HVDC System by Combining Markov and Matrix-Based Methods," Energies, MDPI, vol. 14(11), pages 1-13, May.
    10. Çekyay, B. & Özekici, S., 2010. "Mean time to failure and availability of semi-Markov missions with maximal repair," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1442-1454, December.
    11. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.
    12. Kawahara, Jun & Sonoda, Koki & Inoue, Takeru & Kasahara, Shoji, 2019. "Efficient construction of binary decision diagrams for network reliability with imperfect vertices," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 142-154.
    13. Chetna Choudhary & P. K. Kapur & Sunil K. Khatri & R. Muthukumar & Avinash K. Shrivastava, 2020. "Effort based release time of software for detection and correction processes using MAUT," 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. 11(2), pages 367-378, July.
    14. Kayedpour, Farjam & Amiri, Maghsoud & Rafizadeh, Mahmoud & Shahryari Nia, Arash, 2017. "Multi-objective redundancy allocation problem for a system with repairable components considering instantaneous availability and strategy selection," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 11-20.
    15. Kim, Dong-Seok & Ok, Seung-Yong & Song, Junho & Koh, Hyun-Moo, 2013. "System reliability analysis using dominant failure modes identified by selective searching technique," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 316-331.
    16. 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.
    17. Byun, Ji-Eun & Song, Junho, 2021. "Generalized matrix-based Bayesian network for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    18. 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).
    19. Lu, Qing-Chang & Zhang, Lei & Xu, Peng-Cheng & Cui, Xin & Li, Jing, 2022. "Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    20. Wang, Jinyong & Wu, Zhibo, 2016. "Study of the nonlinear imperfect software debugging model," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 180-192.

    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:165:y:2017:i:c:p:410-421. 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.