PSAM 16 Conference Paper Overview
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Lead Author: Sai Zhang Co-author(s): Fei Xu, Fei.Xu@inl.gov
Zhegang Ma, Zhegang.Ma@inl.gov
| Natural Language Processing-Enhanced Common Cause Failure Data Analysis |
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| Nuclear power plants (NPPs) have a variety of operating records either routinely maintained or conditioned on incident occurrences. While some records are structured, the others are not. Analyzing records with unstructured data (e.g., narratives) can be challenging, not evaluating them would be a missed opportunity since they likely contain valuable operating experience. Given modern artificial intelligence and machine learning capabilities, it could be feasible and efficient to analyze unstructured NPP operating records. This paper presents an exploratory study of using natural language processing (NLP) to enhance common cause failure (CCF) data analysis for NPPs. The NLP-enhanced CCF data analysis attempts to (a) improve understanding of deep-level CCF failure propagation process and (b) compliment limited data pool of CCF events by analyzing non-CCF failure events and estimating their likelihood of evolving into CCF events. Both of these efforts will be implemented using the same approach developed in this study. The NLP-enhanced approach of analyzing a single CCF report includes the following steps: (1) breaking down the report by sentences and identifying causal relationships in each sentence, (2) analyzing and understanding logical relationships among different sentences, and (3) connecting the identified causal relationships in all sentences and forming a failure propagation network. A commonly-used, publicly-available source of unstructured records, licensee event reports, is used in this study. The approach will be applied further to analyze multiple equipment failure reports to evaluate whether a CCF event occurred, how likely an independent failure event can evolve to a CCF event, and to aggregate their failure propagation networks into one big network to draw statistical insights. |
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Author and Presentation Info
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Lead Author Name: Sai Zhang (Sai.Zhang@inl.gov)
Bio: Dr. Sai Zhang works as a probabilistic risk and reliability analyst at Idaho National Laboratory, USA. Her research interests include probabilistic risk assessment, risk-informed analyses and applications, risk-cost optimization, and multi-criterion benefit evaluation for nuclear power plants. She has been a key investigator on a variety of U.S. Nuclear Regulatory Commission- and Department of Energy-sponsored projects including computational support for risk applications, Standardized Plant Analysis Risk (SPAR) modeling, risk-informed analyses for enhanced resilient plant systems, flood barrier testing strategy development, and exploration of artificial intelligence/machine learning in nuclear operating experience. She holds a Ph.D. degree in nuclear engineering from Tsinghua University, China.
Country: United States of America
Company: Idaho National Laboratory
Job Title: Probabilistic Risk and Reliability Analyst
Bio: Dr. Sai Zhang works as a probabilistic risk and reliability analyst at Idaho National Laboratory, USA. Her research interests include probabilistic risk assessment, risk-informed analyses and applications, risk-cost optimization, and multi-criterion benefit evaluation for nuclear power plants. She has been a key investigator on a variety of U.S. Nuclear Regulatory Commission- and Department of Energy-sponsored projects including computational support for risk applications, Standardized Plant Analysis Risk (SPAR) modeling, risk-informed analyses for enhanced resilient plant systems, flood barrier testing strategy development, and exploration of artificial intelligence/machine learning in nuclear operating experience. She holds a Ph.D. degree in nuclear engineering from Tsinghua University, China.
Country: United States of America
Company: Idaho National Laboratory
Job Title: Probabilistic Risk and Reliability Analyst