IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v191y2019ics095183201830084x.html
   My bibliography  Save this article

Procedures to model and solve probabilistic dynamic system problems

Author

Listed:
  • Raoni, Rafael
  • Secchi, Argimiro R.

Abstract

Probabilistic Safety Assessment (PSA), characterized by process-behaviours modelling and event likelihood calculation, has great importance for quantitative risk evaluation. PSA presents some difficulties for implementation, mainly when the analysis of a dynamic process is required. In this work, a set of procedures to formulate and solve Probabilistic Dynamic System Problems (PDSPs) is presented. Such procedures explain how events should be modelled and connected with each other to build a process model that makes it possible to answer two main questions: (i) What is the discrete probability of occurrence of a specific process event? And, given its occurrence (ii) What is the distribution of event time to occurrence? After answering these questions, the event-occurrence probability in a specific length of time, which is the main goal of PSA, is easily calculated. To explain this proposal, two PDSPs are solved: the pressure change in a vessel caused by failure of two valves and the change in holdup tank level caused by failure of two pumps and one valve.

Suggested Citation

  • Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
  • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s095183201830084x
    DOI: 10.1016/j.ress.2019.106554
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183201830084X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2019.106554?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. Dubi, A., 1998. "Analytic approach & Monte Carlo methods for realistic systems analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 243-269.
    3. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2013. "Risk-based design of process systems using discrete-time Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 5-17.
    4. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.
    5. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.
    6. Chiacchio, F. & D’Urso, D. & Manno, G. & Compagno, L., 2016. "Stochastic hybrid automaton model of a multi-state system with aging: Reliability assessment and design consequences," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 1-13.
    7. Flage, R. & Aven, T., 2015. "Emerging risk – Conceptual definition and a relation to black swan type of events," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 61-67.
    8. Di Maio, Francesco & Rai, Ajit & Zio, Enrico, 2016. "A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 9-18.
    9. Berner, C. & Flage, R., 2016. "Strengthening quantitative risk assessments by systematic treatment of uncertain assumptions," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 46-59.
    10. Tombuyses, B. & DeLuca, P.R. & Smidts, C., 1998. "Backward Monte Carlo for probabilistic dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 493-505.
    11. Marseguerra, Marzio & Zio, Enrico, 2009. "Monte Carlo simulation for model-based fault diagnosis in dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 180-186.
    12. Nývlt, Ondřej & Haugen, Stein & Ferkl, Lukáš, 2015. "Complex accident scenarios modelled and analysed by Stochastic Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 539-555.
    13. Karanki, D.R. & Rahman, S. & Dang, V.N. & Zerkak, O., 2017. "Epistemic and aleatory uncertainties in integrated deterministic and probabilistic safety assessment: Tradeoff between accuracy and accident simulations," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 91-102.
    14. Babykina, Génia & Brînzei, Nicolae & Aubry, Jean-François & Deleuze, Gilles, 2016. "Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a Stochastic Hybrid Automaton," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 115-136.
    15. Aven, Terje & Krohn, Bodil S., 2014. "A new perspective on how to understand, assess and manage risk and the unforeseen," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 1-10.
    16. Mandelli, Diego & Yilmaz, Alper & Aldemir, Tunc & Metzroth, Kyle & Denning, Richard, 2013. "Scenario clustering and dynamic probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 146-160.
    17. Domínguez-García, Alejandro D. & Kassakian, John G. & Schindall, Joel E. & Zinchuk, Jeffrey J., 2008. "An integrated methodology for the dynamic performance and reliability evaluation of fault-tolerant systems," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1628-1649.
    18. Li, Jinghui & Mosleh, Ali & Kang, Rui, 2011. "Likelihood ratio gradient estimation for dynamic reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1667-1679.
    19. Chiacchio, F. & Compagno, L. & D'Urso, D. & Manno, G. & Trapani, N., 2011. "Dynamic fault trees resolution: A conscious trade-off between analytical and simulative approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1515-1526.
    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. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    3. Chiacchio, Ferdinando & Iacono, Alessandra & Compagno, Lucio & D'Urso, Diego, 2020. "A general framework for dependability modelling coupling discrete-event and time-driven simulation," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    4. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    5. 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).
    6. Yan-Feng Li & Jinhua Mi & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers," Journal of Risk and Reliability, , vol. 229(6), pages 530-541, December.
    7. Gayathri, P. & Umesh, K. & Ganguli, R., 2010. "Effect of matrix cracking and material uncertainty on composite plates," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 716-728.
    8. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    9. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    10. Tasneem Bani-Mustafa & Nicola Pedroni & Enrico Zio & Dominique Vasseur & Francois Beaudouin, 2020. "A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models," Journal of Risk and Reliability, , vol. 234(6), pages 748-763, December.
    11. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    12. Cheng, Ruijun & Cheng, Yu & Chen, Dewang & Song, Haifeng, 2021. "Online quantitative safety monitoring approach for unattended train operation system considering stochastic factors," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Nikolaos P Ventikos & Konstantinos Louzis, 2023. "Developing next generation marine risk analysis for ships: Bio-inspiration for building immunity," Journal of Risk and Reliability, , vol. 237(2), pages 405-424, April.
    14. 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.
    15. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    16. Sejin Baek & Gyunyoung Heo, 2021. "Application of Dynamic Fault Tree Analysis to Prioritize Electric Power Systems in Nuclear Power Plants," Energies, MDPI, vol. 14(14), pages 1-17, July.
    17. Mi, Jinhua & Li, Yan-Feng & Yang, Yuan-Jian & Peng, Weiwen & Huang, Hong-Zhong, 2016. "Reliability assessment of complex electromechanical systems under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 1-15.
    18. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.
    19. Dong, Zhe & Li, Bowen & Li, Junyi & Huang, Xiaojin & Zhang, Zuoyi, 2022. "Online reliability assessment of energy systems based on a high-order extended-state-observer with application to nuclear reactors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    20. Zheng, Xiaoyu & Tamaki, Hitoshi & Sugiyama, Tomoyuki & Maruyama, Yu, 2022. "Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations," Reliability Engineering and System Safety, Elsevier, vol. 223(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:191:y:2019:i:c:s095183201830084x. 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.