TY - CONF
T1 - Bridging Equipment Reliability Data and Risk-Informed Decisions in a Plant Operation Context
AU - Mandelli, D.
AU - Wang, C.
AU - Lawrence, S.
AU - Morton, D.
AU - Popova, I.
AU - Hess, S.
N1 - Funding Information:
This manuscript has been authored by Battelle Energy Alliance, LLC under Contract No. DE-AC07-05ID14517 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes.
Funding Information:
This manuscript has been authored by Battelle Energy Alliance, LLC under Contract No. DE-AC07-05ID14517 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allowtoehrs toodso, for U.S. Government purposes.
Publisher Copyright:
© 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health and lifecycle data and decision-making (e.g., maintenance scheduling and project prioritization). This article provides a guide for specific use cases that the Risk-Informed Asset Management project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test and surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system-plant reliability models to determine system-plant health and identify components critical to maintaining an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here, the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.
AB - Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health and lifecycle data and decision-making (e.g., maintenance scheduling and project prioritization). This article provides a guide for specific use cases that the Risk-Informed Asset Management project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test and surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system-plant reliability models to determine system-plant health and identify components critical to maintaining an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here, the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.
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M3 - Paper
AN - SCOPUS:85146249873
T2 - 16th International Conference on Probabilistic Safety Assessment and Management, PSAM 2022
Y2 - 26 June 2022 through 1 July 2022
ER -