PSAM 16 - Session Th14 Overview

PSAM 16 Conference Session Th14 Overview

Session Chair: Craig Primer (craig.primer@inl.gov)

Paper 1 VI170

Lead Author: Vivek Agarwal Co-author(s): Koushik A. Manjunatha, Andrei V. Gribok, Torrey J. Mortenson, and Harry Palas

Scalable Risk-Informed Predictive Maintenance Strategy for Operating Nuclear Power Plants
Over the years, the nuclear fleet has relied on labor-intensive, time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. The primary objective of the research presented in this paper is to develop scalable technologies deployable across plant assets and the nuclear fleet in order to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). A well-constructed risk-informed PdM approach for an identified plant asset was developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), risk model, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology-driven PdM program, eliminating unnecessary O&M costs. The research and development approach presented in the paper is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group (PSEG) Nuclear LLC. This paper presents the results of analyzing the heterogeneous data associated with the circulating water system (CWS) from both the Salem and Hope Creek NPP sites. Fault modes present in the data were identified based on logs and correlated with data to develop salient fault signatures associated with each fault mode. The fault signatures are used to develop diagnostic models using scalable predictive analytics and integrated with plant-level risk and economic models. The paper also outlines the development of a user-centric visualization application to ensure the right information is available to the right person, in the right format, and at the right time. The research outcomes presented in this paper lay the foundation and provide a much-needed technical basis to start focusing on additional needs such as explainability and trustworthiness of ML- and AI-based technologies as part of future research.

Scalable Risk-Informed Predictive Maintenance Strategy for Operating Nuclear Power Plants

Over the years, the nuclear fleet has relied on labor-intensive, time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. The primary objective of the research presented in this paper is to develop scalable technologies deployable across plant assets and the nuclear fleet in order to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). A well-constructed risk-informed PdM approach for an identified plant asset was developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), risk model, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology-driven PdM program, eliminating unnecessary O&M costs. The research and development approach presented in the paper is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group (PSEG) Nuclear LLC. This paper presents the results of analyzing the heterogeneous data associated with the circulating water system (CWS) from both the Salem and Hope Creek NPP sites. Fault modes present in the data were identified based on logs and correlated with data to develop salient fault signatures associated with each fault mode. The fault signatures are used to develop diagnostic models using scalable predictive analytics and integrated with plant-level risk and economic models. The paper also outlines the development of a user-centric visualization application to ensure the right information is available to the right person, in the right format, and at the right time. The research outcomes presented in this paper lay the foundation and provide a much-needed technical basis to start focusing on additional needs such as explainability and trustworthiness of ML- and AI-based technologies as part of future research.

Paper VI170 | Download the paper file. |

Name: Vivek Agarwal (vivek.agarwal@inl.gov)

Bio: Dr. Vivek Agarwal is a Senior Research Scientist and Technical Lead of the Fission Battery Initiative. He specializes in crosscutting applications and advancement of inter-disciplinary research to enable resilient real-time measurement and control of process variables within the nuclear and other critical industries. Dr. Agarwal received a Bachelor of Engineering in electrical engineering from University of Madras, India in 2001, M.S. in electrical engineering from University of Tennessee, Knoxville in 2005, and Ph.D. in nuclear engineering from Purdue University in 2011. He joined INL as a research in 2011 and since then has lead projects under multiple DOE programs such as Light Water Research Sustainability, Nuclear Energy Enabling Technologies – Advanced Sensors and Instrumentation Program, Technology Transition Office, and others. Dr. Agarwal was awarded 2019 Presidential Early Career Awards for Scientists and Engineers, and 2016 Laboratory Director Early Career Achievement Award.

Country: USA
Company: Idaho National Laboratory
Job Title: Senior Research Scientist

Paper 2 PL113

Lead Author: Plínio Ramos Co-author(s): July B. Macedo - julybias@gmail.com Caio B. S. Maior - caio.maior@ufpe.br Márcio C. Moura - marcio.cmoura@ufpe.br Isis D. Lins - isis.lins@ufpe.br

Combining BERT with numerical features to classify injury leave based on accident description
Workplace safety is a major concern in many industries. In this context, accident investigation reports provide useful knowledge to support companies to propose preventive and mitigative measures. However, the information presented in accident reports databases is normally large, complex, also filled out with redundant data. Thus, a complete human review of the entire database is arduous, considering numerous reports produced by a company. Therefore, natural language processing (NLP)-based techniques are suitable for analyzing a massive amount of textual information. In this paper, we adopted NLP techniques to determine whether or not an injury leave would be expected. The methodology was applied on 648 accident reports collected from an actual hydroelectric power company and focused on the accident agent categories. We employ Bidirectional Encoder Representations from Transformers (BERT), a state-of-art natural language processing method, to tackle the aforementioned problem. The text representations provided by BERT model were combined with numerical and binary features extracted from the accident reports. These combined features are input to an MLP that predicts the occurrence of the accident leave for a given accident. Indeed, accident investigation reports to provide a useful knowledge to support decisions in the safety context.

Combining BERT with numerical features to classify injury leave based on accident description

Workplace safety is a major concern in many industries. In this context, accident investigation reports provide useful knowledge to support companies to propose preventive and mitigative measures. However, the information presented in accident reports databases is normally large, complex, also filled out with redundant data. Thus, a complete human review of the entire database is arduous, considering numerous reports produced by a company. Therefore, natural language processing (NLP)-based techniques are suitable for analyzing a massive amount of textual information. In this paper, we adopted NLP techniques to determine whether or not an injury leave would be expected. The methodology was applied on 648 accident reports collected from an actual hydroelectric power company and focused on the accident agent categories. We employ Bidirectional Encoder Representations from Transformers (BERT), a state-of-art natural language processing method, to tackle the aforementioned problem. The text representations provided by BERT model were combined with numerical and binary features extracted from the accident reports. These combined features are input to an MLP that predicts the occurrence of the accident leave for a given accident. Indeed, accident investigation reports to provide a useful knowledge to support decisions in the safety context.

Paper PL113 | Download the paper file. | Download the presentation PowerPoint file.

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Paper 3 AH327

Lead Author: Ahmad Al Rashdan

Mapping inspection procedure requirements into data metrics for automation-assisted inspection preparation
In the nuclear industry, regulatory compliance activities constitute an appreciable portion of a nuclear plant's data collection and analysis efforts, and therefore represent a significant portion of a nuclear utility operations and maintenance budget, with regulatory inspections as a major component. These inspections involve many different types of information needed by the U.S. Nuclear Regulatory Commission’s (NRC) Reactor Oversight Program. The Department of Energy’s Light Water Reactor Sustainability (LWRS) program launched an effort to automate data collection and analysis for regulatory inspection preparation. The effort would also assess the ability of automation technologies to provide potentially more efficient means for nuclear utilities to verify and demonstrate regulatory compliance. A key aspect of automating this process is to convert plant performance related to each of the inspection requirements into quantifiable data metrics. The development of those data metrics is discussed using the NRC problem identification and resolution inspection as a use case.

Mapping inspection procedure requirements into data metrics for automation-assisted inspection preparation

In the nuclear industry, regulatory compliance activities constitute an appreciable portion of a nuclear plant's data collection and analysis efforts, and therefore represent a significant portion of a nuclear utility operations and maintenance budget, with regulatory inspections as a major component. These inspections involve many different types of information needed by the U.S. Nuclear Regulatory Commission’s (NRC) Reactor Oversight Program. The Department of Energy’s Light Water Reactor Sustainability (LWRS) program launched an effort to automate data collection and analysis for regulatory inspection preparation. The effort would also assess the ability of automation technologies to provide potentially more efficient means for nuclear utilities to verify and demonstrate regulatory compliance. A key aspect of automating this process is to convert plant performance related to each of the inspection requirements into quantifiable data metrics. The development of those data metrics is discussed using the NRC problem identification and resolution inspection as a use case.

Paper AH327 | |

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: USA
Company: Idaho National Laboratory
Job Title: Senior Research and Development Scientist