Automated Identification of Causal Relationships in Nuclear Power Plant Event Reports

Zhao, Yunfei; Diao, Xiaoxu; Huang, Jonathan; Smidts, Carol

doi:10.1080/00295450.2019.1580967

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…The deep learning researchers mainly use Natural Language Processing (NLP) algorithms or LSTM algorithms to process signal data for prediction and classification. Based on NLP techniques, a rulebased expert system, Causal Relationship Identification (CaRI), is proposed in Zhao et al [37]. The proposed method is applied to analyze the abstract section of the reports from the U.S. Nuclear Regulatory Commission Licensee Event Report database.…”

Section: Deep Learning For Nuclear Industry and Dmusmentioning

confidence: 99%

Digital Mock-Ups for Nuclear Decommissioning: A Survey on Existing Simulation Tools for Industry Applications

Caliskanelli

2023

RAEJ

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The maturation of Virtual Reality software introduces new avenues of nuclear decommissioning research. Digital Mockups are an emerging technology which provide a virtual representation of the environment, objects or processes, supporting the whole lifecycle of product development and operations. This paper provides a survey on currently available simulation tools to design digital mock-ups required for safe remote decommissioning activities in the nuclear industry. The survey looks at eleven simulation tools; CoppeliaSim, Gazebo Classic, Gazebo (Ignition), Nvidia Omniverse Isaac Sim, WeBots, Choreonoid, AGX Dynamics, MORSE, VR4Robots, RoboDK, and Toia. Using the available documentation, the different capabilities of these software packages were assessed for their suitability to nuclear decommissioning, such as environment simulation, haptic interfaces, and general usability.

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Section: Deep Learning For Nuclear Industry and Dmusmentioning

confidence: 99%

Digital Mock-Ups for Nuclear Decommissioning: A Survey on Existing Simulation Tools for Industry Applications

Caliskanelli

2023

RAEJ

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“…To recognize free-text data and extract implied information inside ML algorithms such as NLP, supervised and unsupervised ML is applied. (Zhao et al 2019) utilized NLP to extract the causal relationships among failure-contributing factors from free-text reports. (Moura et al 2017) applied unsupervised ML (clustering) to validate risk studies using information from past major accidents.…”

Section: Ai/ml In Nuclear Safety and Risk Analysismentioning

confidence: 99%

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Bao

Zhang

et al. 2022

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This report presents the project Idaho National Laboratory conducted for the Nuclear Regulatory Commission (NRC) to explore the advanced computational tools and techniques, such as artificial intelligence (AI) and machine learning (ML), for operating nuclear plants. The report reviews the nuclear data sources, with the focus on operating experience data, that could be applied by advanced computational tools and techniques. Plant-specific and generic (national and international) data from different sources are described. The report describes the relationships between statistics and AI/ML and then introduces the most widely used AI/ML algorithms in both supervised and unsupervised learning. The report reviews the recent applications of advanced computational tools and techniques in various fields of nuclear industry, such as reactor system design and analysis, plant operation and maintenance, and nuclear safety and risk analysis. The report presents the insights from the project on the potential applicability of AI/ML techniques in improving advanced computational capabilities, how the advanced tools and techniques could contribute to the understanding of safety and risk, and what information would be needed to provide meaningful insights to decision makers.The report also documents an NRC survey on the current state of commercial nuclear power operations relative to the use of AI and ML tools as well as the role of AI/ML tools in nuclear power operations, which was published by the NRC as in the Federal Register Notice NRC-2021-0048 in April 2021. A summary of the survey, including the survey questions, survey participants, survey responses, and the conclusions and insights derived from the survey, is provided in the report.Finally, the report investigates potential applications of using AI/ML in operating nuclear power plants and advanced reactors (both advanced light-water reactors and advanced non-light-water reactors) to improve nuclear plant safety and efficiency. Three main application fields are considered: plant safety and security assessments; plant degradation modeling, fault and accident diagnosis and prognosis; and plant operation and maintenance efficiency improvement. v TABLE OF CONTENTS ABSTRACT .

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“…However, the extensive event reports in various text formats pose a great challenge to the comprehensive analysis. Based on the Stanford's CoreNLP API [114] , Zhao et al [115] proposed a rule-based expert system called Causal Relationship Identification (CaRI), to identify the causal relationship between events by analyzing the abstract section of the reports from the U.S. Nuclear Regulatory Commission Licensee Event Report database [116] . Experiments on the CaRI system show that most causality relationships can be captured and analyzed automatically.…”

Section: Sequence Data Processing In Nuclear Industrymentioning

confidence: 99%

Deep learning in nuclear industry: A survey

Tang

Gao

et al. 2022

Big Data Min. Anal.

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As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy.Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medical treatment, radiation processing, and so on. With the development of artificial intelligence and the proposal of "Industry 4.0", more and more artificial intelligence technologies are introduced into the nuclear industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, deep learning, as an important technology of artificial intelligence, has made amazing progress in theoretical and applied research in the nuclear industry, which vigorously promotes the development of informatization, digitization, and intelligence of the nuclear industry. In this paper, we first simply comb and analyze the intelligent demand scenarios in the whole industrial chain of the nuclear industry. Then, we discuss the data types involved in the nuclear industry chain. After that, we investigate the research status of deep learning in the application fields corresponding to different data types in the nuclear industry. Finally, we discuss the limitation and unique challenges of deep learning in the nuclear industry and the future direction of the intelligent nuclear industry.

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Automated Identification of Causal Relationships in Nuclear Power Plant Event Reports

Cited by 10 publications

References 11 publications

Digital Mock-Ups for Nuclear Decommissioning: A Survey on Existing Simulation Tools for Industry Applications

Digital Mock-Ups for Nuclear Decommissioning: A Survey on Existing Simulation Tools for Industry Applications

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Deep learning in nuclear industry: A survey

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