PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Ahmad Al Rashdan Co-author(s): Brian Wilcken Brian.Wilcken@inl.gov Kellen Giraud Kellen.Giraud@inl.gov Svetlana Lawrence Svetlana.Lawrence@inl.gov

Using Automated Trending to Inform Nuclear Power Plant Compliance
To remain competitive in current and future energy markets, the nuclear power industry must reduce operating costs while improving operational performance through comprehensive plant modernization and by transforming the model of operations from being historically labor-centric to one that is technology-centric. Many nuclear power plant (NPP) activities could be transformed using recent advances in automation. Specifically, activities performed in an NPP to achieve or demonstrate compliance with regulatory requirements could be performed more efficiently, both from a licensee and a regulator point of view. A corrective action program (CAP) is a key component of NPP compliance and performance-tracking activities. In addition to documenting and tracking the resolution of issues that occur in NPPs, a CAP is a primary resource for monitoring the trending of issues and causes of undesirable events across a plant, fleet of plants, or even the industry as a whole. Those conditions are documented in condition reports (CRs). Thus, CRs contain invaluable information about plant performance. However, CR analysis is one of the expensive aspects of regulatory compliance, due to the manual and time-consuming review and analysis involved. Trending the occurrence frequency and causes of certain issues across a plant, fleet of plants, or even the industry as a whole can lead to insights that are of particular interest regarding detecting degraded performance (e.g., increasing occurrences of a specific issue). Such trends accumulate the knowledge gained from the occurrence of issues and causes over time to generate conclusions for areas the plant should improve, or the regulator should monitor. Significant trends can be used as a new tool to risk-inform the compliance process. Automating the trending process to detect issues/causes found in CRs can reveal hidden trends that might not be uncovered manually. To achieve this objective, natural language processing (NLP) and machine learning (ML) methods were used to mine hundreds of thousands of CRs from multiple nuclear power utilities to generate a list of topics that cumulatively can describe any CRs created by the plant. Those topics resemble the typical keyword assignment applied to documents. Once a set of topics is automatically assigned to each CR, the CRs can be filtered for a specific combination of topics in order to extract and track any trends regarding a specific issue. Such trends can be automatically analyzed so that only trends of particular interest are presented for human consideration.

Using Automated Trending to Inform Nuclear Power Plant Compliance

To remain competitive in current and future energy markets, the nuclear power industry must reduce operating costs while improving operational performance through comprehensive plant modernization and by transforming the model of operations from being historically labor-centric to one that is technology-centric. Many nuclear power plant (NPP) activities could be transformed using recent advances in automation. Specifically, activities performed in an NPP to achieve or demonstrate compliance with regulatory requirements could be performed more efficiently, both from a licensee and a regulator point of view. A corrective action program (CAP) is a key component of NPP compliance and performance-tracking activities. In addition to documenting and tracking the resolution of issues that occur in NPPs, a CAP is a primary resource for monitoring the trending of issues and causes of undesirable events across a plant, fleet of plants, or even the industry as a whole. Those conditions are documented in condition reports (CRs). Thus, CRs contain invaluable information about plant performance. However, CR analysis is one of the expensive aspects of regulatory compliance, due to the manual and time-consuming review and analysis involved. Trending the occurrence frequency and causes of certain issues across a plant, fleet of plants, or even the industry as a whole can lead to insights that are of particular interest regarding detecting degraded performance (e.g., increasing occurrences of a specific issue). Such trends accumulate the knowledge gained from the occurrence of issues and causes over time to generate conclusions for areas the plant should improve, or the regulator should monitor. Significant trends can be used as a new tool to risk-inform the compliance process. Automating the trending process to detect issues/causes found in CRs can reveal hidden trends that might not be uncovered manually. To achieve this objective, natural language processing (NLP) and machine learning (ML) methods were used to mine hundreds of thousands of CRs from multiple nuclear power utilities to generate a list of topics that cumulatively can describe any CRs created by the plant. Those topics resemble the typical keyword assignment applied to documents. Once a set of topics is automatically assigned to each CR, the CRs can be filtered for a specific combination of topics in order to extract and track any trends regarding a specific issue. Such trends can be automatically analyzed so that only trends of particular interest are presented for human consideration.

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Author and Presentation Info

Presentation only, a full paper is not available.

Lead Author Name: Ahmad Al Rashdan (ahmad.alrashdan@inl.gov)

Bio: Ahmad Al Rashdan, Ph.D., is currently a senior research and development scientist at INL. Dr. Al Rashdan holds a Ph.D. in nuclear engineering from Texas A&M University, a M.Sc. in information technology and automation systems from Esslingen University of Applied Science in Germany, and a B.Sc. in mechanical engineering from Jordan University of Science and Technology in Jordan. He has more than 15 years of industrial and research experience. His experience includes automated work processes using AI methods and advanced analytics, online condition monitoring of nuclear systems, control-systems design and development, anomalies detection, and automated modeling and simulation. Dr. Al Rashdan is an active contributor to and organizer of several DOE events and scientific conferences. He authored or co-authored more than 60 technical reports and journal papers and 7 patent applications and is a senior member of the IEEE and a member of the American Nuclear Society.

Country: United States of America
Company: Idaho National Laboratory
Job Title: Senior Research and Development Scientist