Methods of Data Collection in Nuclear Power Plants

Rashdan, Ahmad Al; Germain, Shawn St.

doi:10.1080/00295450.2019.1610637

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Nuclear data sources can be categorized in different ways. For example, (Atwood et al 2003) utilizes two types of data sources, plant-specific and generic, to produce various parameter estimates that are needed in a PRA, and (Al Rashdan et al 2019, Al Rashdan andSt. Germain 2019) categorize fifteen typical NPP data sources based on their data-collection methods.…”

Section: Nuclear Data Sourcesmentioning

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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Section: Nuclear Data Sourcesmentioning

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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“…The information contained in NPP textual ER data can either describe the occurrence of abnormal events (e.g., system, structure and components [SSC] failure or observed degradation)-with such documents being referred to here as issue reports (IRs)-or the conduct of maintenance or surveillance activities (referred to here as work orders [WOs]). Only recently has the analysis of textual data been investigated via machine learning (ML) methods [10][11][12][13] designed to assess the nature of the data (e.g., safety or non-safety related) by employing supervised or semi-supervised ML models [14,15]. This paper primarily focuses on applying natural language processing (NLP) methods [16][17][18][19] for ER data analysis in order to support robust decision-making in a plant operations context.…”

Section: Introductionmentioning

confidence: 99%

“…Here, the objective in analyzing textual ER data is to move away from supervised/semisupervised ML model analysis tools [10][11][12][13] and to instead automate the extraction of quantitative knowledge from textual data in order to assist system engineers in assessing SSC health trends and identify SSC anomalous behaviors. Knowledge extraction [20][21][22][23][24] is a very broad concept whose definition may vary depending on the application context.…”

Section: Introductionmentioning

confidence: 99%

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Wang,

Mandelli,

Cogliati

2024

Energies

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Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

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“…On the other hand, the information of most indicators (such as instruments or lights) and controls (such as buttons and knobs) in the analog MCR of NPPs is manually recorded [11]. These records provide a basis for post analysis of operational events (including human error analysis).…”

Section: Introductionmentioning

confidence: 99%

Human Performance Detection Using Operator Action Log of Nuclear Power Plant

et al. 2023

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The introduction of digital technologies into the main control room of a nuclear power plant also introduces new human errors. The operator log records the control information of operators on systems and equipment, and provides an important data source for the retrospective investigation of operating events in a nuclear power plant. A traditional operator log review is conducted manually, which has some major problems, such as being time-consuming and inefficient. This paper proposes an automatic detection method for operator logs, which models an operating procedure at three levels, including procedure, step and action. Such a model clarifies the overall logic and basic attributes of the operating procedure, and can be used as a standardized template of a control action sequence to compare with the actual operation actions in the operator log, so as to identify possible human performance deviations. This paper explains the method, and discusses the advantages and limitations of the proposed method.

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Methods of Data Collection in Nuclear Power Plants

Cited by 11 publications

References 5 publications

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

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

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Human Performance Detection Using Operator Action Log of Nuclear Power Plant

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