IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v111y2013icp206-216.html
   My bibliography  Save this article

Solving dynamic flowgraph methodology models using binary decision diagrams

Author

Listed:
  • Bjorkman, Kim

Abstract

Dynamic flowgraph methodology (DFM) is a computationally challenging approach to the reliability analysis of dynamic systems with feedback loops. To improve the computational efficiency of DFM modelling, we propose a new approach, based on binary decision diagrams (BDDs), to solving DFM models. The objective of DFM analysis is to identify the root causes of a postulated top event. The result is a set of prime implicants that represent system faults resulting from diverse combinations of software logic errors, hardware failures, human errors and adverse environmental conditions. Two approaches to solving prime implicants have been implemented in software called YADRAT. The first approach is based on meta-products, and the second on zero-suppressed BDDs (ZBDD). Both approaches have been used previously in fault tree analysis. In this work, the ideas of prime implicant computations are adapted to a dynamic reliability analysis approach combined with multi-valued logic. The computational efforts required for the two approaches are compared by analysing three example systems. The results of the comparison show that BDDs are applicable in DFM computation and that in particular the ZBDD-based approach can solve moderately sized DFM models in a reasonable time.

Suggested Citation

  • Bjorkman, Kim, 2013. "Solving dynamic flowgraph methodology models using binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 206-216.
    • Handle: RePEc:eee:reensy:v:111:y:2013:i:c:p:206-216
    DOI: 10.1016/j.ress.2012.11.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832012002384
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2012.11.009?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. Bucci, Paolo & Kirschenbaum, Jason & Mangan, L. Anthony & Aldemir, Tunc & Smith, Curtis & Wood, Ted, 2008. "Construction of event-tree/fault-tree models from a Markov approach to dynamic system reliability," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1616-1627.
    2. R Remenyte-Prescott & J Andrews, 2008. "Analysis of non-coherent fault trees using ternary decision diagrams," Journal of Risk and Reliability, , vol. 222(2), pages 127-138, June.
    3. 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.
    4. Doguc, Ozge & Ramirez-Marquez, Jose Emmanuel, 2009. "A generic method for estimating system reliability using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 542-550.
    5. A B Rauzy, 2008. "Some disturbing facts about depth-first left-most variable ordering heuristics for binary decision diagrams," Journal of Risk and Reliability, , vol. 222(4), pages 573-582, December.
    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. Di Maio, Francesco & Baronchelli, Samuele & Zio, Enrico, 2014. "Hierarchical differential evolution for minimal cut sets identification: Application to nuclear safety systems," European Journal of Operational Research, Elsevier, vol. 238(2), pages 645-652.
    2. McNelles, Phillip & Zeng, Zhao Chang & Renganathan, Guna & Lamarre, Greg & Akl, Yolande & Lu, Lixuan, 2016. "A comparison of Fault Trees and the Dynamic Flowgraph Methodology for the analysis of FPGA-based safety systems Part 1: Reactor trip logic loop reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 135-150.
    3. McNelles, Phillip & Renganathan, Guna & Zeng, Zhao Chang & Chirila, Marius & Lu, Lixuan, 2019. "A comparison of fault trees and the Dynamic Flowgraph Methodology for the analysis of FPGA-based safety systems part 2: Theoretical investigations," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 60-83.
    4. Zaitseva, Elena & Levashenko, Vitaly & Kostolny, Jozef, 2015. "Importance analysis based on logical differential calculus and Binary Decision Diagram," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 135-144.
    5. Mo, Yuchang & Xing, Liudong & Amari, Suprasad V. & Bechta Dugan, Joanne, 2015. "Efficient analysis of multi-state k-out-of-n systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 95-105.
    6. Reed, Sean, 2017. "An efficient algorithm for exact computation of system and survival signatures using binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 257-267.

    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. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2012. "Dynamic risk analysis using bow-tie approach," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 36-44.
    2. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. 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.
    4. Aldemir, T. & Guarro, S. & Mandelli, D. & Kirschenbaum, J. & Mangan, L.A. & Bucci, P. & Yau, M. & Ekici, E. & Miller, D.W. & Sun, X. & Arndt, S.A., 2010. "Probabilistic risk assessment modeling of digital instrumentation and control systems using two dynamic methodologies," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1011-1039.
    5. Zheng, Xiaoyu & Yamaguchi, Akira & Takata, Takashi, 2013. "α-Decomposition for estimating parameters in common cause failure modeling based on causal inference," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 20-27.
    6. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    7. 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).
    8. Garg, Vipul & Vinod, Gopika & Kant, Vivek, 2023. "Auto-CREAM: Software application for evaluation of HEP with basic and extended CREAM for PSA studies," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    9. Marhavilas, P.K. & Koulouriotis, D.E., 2012. "A combined usage of stochastic and quantitative risk assessment methods in the worksites: Application on an electric power provider," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 36-46.
    10. 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).
    11. Farshad BahooToroody & Saeed Khalaj & Leonardo Leoni & Filippo De Carlo & Gianpaolo Di Bona & Antonio Forcina, 2021. "Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
    12. Y-F Wang & M Xie & M S Habibullah & K-M Ng, 2011. "Quantitative risk assessment through hybrid causal logic approach," Journal of Risk and Reliability, , vol. 225(3), pages 323-332, September.
    13. 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).
    14. Saurabh Prabhu & Mohammad Javanbarg & Marc Lehmann & Sez Atamturktur, 2019. "Multi-peril risk assessment for business downtime of industrial facilities," 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. 97(3), pages 1327-1356, July.
    15. Paglioni, Vincent P. & Groth, Katrina M., 2022. "Dependency definitions for quantitative human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    16. 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).
    17. Greco, Salvatore F. & Podofillini, Luca & Dang, Vinh N., 2021. "A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    18. Zheng, Yi-Xuan & Xiahou, Tangfan & Liu, Yu & Xie, Chaoyang, 2021. "Structure function learning of hierarchical multi-state systems with incomplete observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    19. Wu, Daohua & Zheng, Wei, 2018. "Formal model-based quantitative safety analysis using timed Coloured Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 62-79.
    20. Andrews, John & Fecarotti, Claudia, 2017. "System design and maintenance modelling for safety in extended life operation," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 95-108.

    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:206-216. 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.