Physics of failure, predictive modeling & data analytics for LOCA frequency

Nicholas O'Shea, Justin Pence, Zahra Mohaghegh, Ernie Kee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents: (a) the Data-Theoretic methodology as part of an ongoing research which integrates Physics-of-Failure (PoF) theories and data analytics to be applied in Probabilistic Risk Assessment (PRA) of complex systems and (b) the status of the application of the proposed methodology for the estimation of the frequency of the location-specific loss-of-coolant accident (LOCA), which is a critical initiating event in PRA and one of the challenges of the risk-informed resolution for Generic Safety Issue 191 (GSI-191) [1]. The proposed methodology has the following unique characteristics: (1) it uses predictive causal modeling along with sensitivity and uncertainty analysis to find the most important contributing factors in the PoF models of failure mechanisms. This model-based approach utilizes importance-ranking techniques, scientifically reduces the number of factors, and focuses on a detailed quantification strategy for critical factors rather than conducting expensive experiments and time-consuming simulations for a large number of factors. Th is adds validity and practicality to the proposed methodology. (2) Because of the evolving nature of computational power and information-sharing technologies, the Data-Theoretic method for PRA expands the classical approach of data extraction and implementation for risk analysis. It utilizes advanced data analytic techniques (e.g., data mining and text mining) to extract risk and reliability information from diverse data sources (academic literature, service data, regulatory and laboratory reports, expert opinion, maintenance logs, news, etc.) and executes them in theory-based PoF networks. (3) The Data-Theoretic approach uses comprehensive underlying PoF theory to avoid potentially misleading results from use of solely data-oriented approaches, as well as support the completeness of the contextual physical factors and the accuracy of their causal relationships. (4) When the important factors are identified, the Data-Theoretic approach applies all potential theory-based techniques (e.g., simulation and experimentation).

Original languageEnglish (US)
Title of host publicationRAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479967025
DOIs
StatePublished - May 8 2015
Event61st Annual Reliability and Maintainability Symposium, RAMS 2015 - Palm Harbor, United States
Duration: Jan 26 2015Jan 29 2015

Publication series

NameProceedings - Annual Reliability and Maintainability Symposium
Volume2015-May
ISSN (Print)0149-144X

Other

Other61st Annual Reliability and Maintainability Symposium, RAMS 2015
CountryUnited States
CityPalm Harbor
Period1/26/151/29/15

Keywords

  • LOCA frequency
  • Probabilistic Physics of Failure
  • Probabilistic Risk Assessment
  • data analytics
  • predictive causal modeling

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mathematics(all)
  • Computer Science Applications

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