RISA Industry Use Case Analysis

Szilard, Ronaldo; Prescott, Steven; Mandelli, Diego; Hess, Stephen; Gaertner, John P.; Zhang, Hongbin

doi:10.2172/1495184

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…These tools, methods and algorithms employ data mining and machine learning models which greatly advance state-of-the-art PRA practices. During FY18, along with the restructuring of the LWRS program, RISMC became RISA (Risk Informed Safety Analysis) with a more specific goal of directly targeting industry relevant applications [2]. As part of this new direction, special attention has been given to economic risk analysis that can be applied to existing NPPs.…”

Section: Introductionmentioning

confidence: 99%

Cost Risk Analysis Framework (CRAFT): An Integrated Risk Analysis Tool and its Application in an Industry Use Case

Mandelli

Wang

Shad

et al. 2018

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This report presents the initial application design of the Cost Risk Analysis Framework Tool (CRAFT). At the beginning, the concept of CRAFT was formulated from a need to perform economic risk analysis that can be quickly deployable through the nuclear industry to evaluate the risk associated to capital projects and to perform plant asset management. The design evolved into a framework that can integrate several forms of risk (e.g., not just economic, but also safety related) into a single analysis. In order to reduce the research, development and deployment time, we have decided to use the Risk Analysis Virtual ENvironment (RAVEN) statistical framework as a basis for the CRAFT architecture. In this report we summarize the most recent developments accomplished during the second part of FY18 for the two use cases of the Cost and Risk Categorization Applications path of the RISA project. These two use cases directly target plant health and plant capital SSC management. Regarding the plant health management use case, we have started to investigate methods to extract information from text-based data: text data mining. The Risk Informed Asset management is the main target of this report and several methods are here shown in order to solve the capital SSC replacement issue. This has been performed by presenting: several models designed to determine the effective cost through the lifetime of a capital SSCs, and the optimization algorithms that will be employed to determine the optimal replacement schedule of a given set of capital SSC. The interface between RAVEN and the PRA code SAPHIRE is the first step toward the integration of several plant risk models (e.g., economic and safety) and plant data (e.g., databases from the plant maintenance and diagnostics center) into a single analysis framework. In this respect, RAVEN is the ideal platform to connect several models and to manage data streaming among them.

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Section: Introductionmentioning

confidence: 99%

Cost Risk Analysis Framework (CRAFT): An Integrated Risk Analysis Tool and its Application in an Industry Use Case

Mandelli

Wang

Shad

et al. 2018

Self Cite

View full text Add to dashboard Cite

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Data-theoretic approach for socio-technical risk analysis: Text mining licensee event reports of U.S. nuclear power plants

et al. 2020

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LERCause: Deep learning approaches for causal sentence identification from nuclear safety reports

Kim,

Lee

et al. 2024

PLoS ONE

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Identifying causal sentences from nuclear incident reports is essential for advancing nuclear safety research and applications. Nonetheless, accurately locating and labeling causal sentences in text data is challenging, and might benefit from the usage of automated techniques. In this paper, we introduce LERCause, a labeled dataset combined with labeling methods meant to serve as a foundation for the classification of causal sentences in the domain of nuclear safety. We used three BERT models (BERT, BioBERT, and SciBERT) to 10,608 annotated sentences from the Licensee Event Report (LER) corpus for predicting sentence labels (Causal vs. non-Causal). We also used a keyword-based heuristic strategy, three standard machine learning methods (Logistic Regression, Gradient Boosting, and Support Vector Machine), and a deep learning approach (Convolutional Neural Network; CNN) for comparison. We found that the BERT-centric models outperformed all other tested models in terms of all evaluation metrics (accuracy, precision, recall, and F1 score). BioBERT resulted in the highest overall F1 score of 94.49% from the ten-fold cross-validation. Our dataset and coding framework can provide a robust baseline for assessing and comparing new causal sentences extraction techniques. As far as we know, our research breaks new ground by leveraging BERT-centric models for causal sentence classification in the nuclear safety domain and by openly distributing labeled data and code to enable reproducibility in subsequent research.

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RISA Industry Use Case Analysis

Cited by 3 publications

References 6 publications

Cost Risk Analysis Framework (CRAFT): An Integrated Risk Analysis Tool and its Application in an Industry Use Case

Cost Risk Analysis Framework (CRAFT): An Integrated Risk Analysis Tool and its Application in an Industry Use Case

Data-theoretic approach for socio-technical risk analysis: Text mining licensee event reports of U.S. nuclear power plants

LERCause: Deep learning approaches for causal sentence identification from nuclear safety reports

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