A probabilistic model for online scenario labeling in dynamic event tree generation

Daniya Zamalieva, Alper Yilmaz and Tunc Aldemir

Reliability Engineering and System Safety, 2013, vol. 120, issue C, 18-26

Abstract: Dynamic event trees provide a wide coverage of possible system evolution sequences (scenarios) and may lead to the simulation of thousands of scenarios following a single initiating event. The large number of scenarios can be a burden in terms of computational time and storage requirements. However, not all of the scenarios are equally significant. From a safety point, failure scenarios or the scenarios that lead to undesirable consequences are more important than the scenarios that represent normal system evolution (non-failure scenarios). A method is presented for online labeling of non-failure scenarios. Since the number of non-failure scenarios is usually much larger than that of failure scenarios, substantial computational savings could be obtained if the non-failure scenarios can be identified and not pursued by the simulator. First, the parameters of a hidden Markov model that represents the behavior of non-failure scenarios are learned using the examples of the non-failure scenarios. Next, the failure behavior with respect to the non-failure model is learned using sample failure scenarios. During the succeeding system simulations, a scenario is labeled as non-failure if its evolution path is more likely to fit the constructed model than the learned failure behavior. Experiments using RELAP5/3D model of a fast reactor utilizing an RVACS (Reactor Vessel Auxiliary Cooling System) passive decay heat removal system show that the proposed method is capable of correctly labeling over 85% of non-failure scenarios without mislabeling the failure scenarios and provide time savings of at least 55%. We also investigate the sensitivity of the proposed labeling scheme depending on the number of hidden states in HMM and the nature of the state variables used for scenario representation.

Keywords: Transient analysis; Scenario labeling; Dynamic PRA (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832013000720
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:120:y:2013:i:c:p:18-26

DOI: 10.1016/j.ress.2013.02.028

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().