Development of an End State Vision to Implement Digital Monitoring in Nuclear Plants

Cody Walker, Vivek Agarwal, Pradeep Ramuhalli, Nancy J. Lybeck, Michael Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nuclear plant sites collect and store large volumes of data gathered from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning (ML) and artificial intelligence (AI) technologies to provide diagnostic and prognostic capabilities within the nuclear power industry, thus reducing operations and maintenance (O&M) costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature plant closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging ML and AI technologies to develop data-driven algorithms that better diagnose and predict potential faults within the system. Improved model accuracy can help reduce unnecessary maintenance and foster more efficient planning of future maintenance, thereby lowering the costs associated with parts, labor, and costly planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from routine-based monitoring to online monitoring by taking advantage of advancements in sensors and wireless communication technologies. Advancements in data storage, mapping, management, and analytics would inform the transition from onsite- to cloud-based computing and storage services. Online monitoring would reduce the number of operator man-hours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained — all while scaling to both computational and storage demands. This paper summarizes an end state vision of how to shift from costly, labor-intensive preventative maintenance to cost-effective predictive maintenance.

Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
EditorsChetan Kulkarni, Abhinav Saxena
PublisherPrognostics and Health Management Society
Edition1
ISBN (Electronic)9781936263370
DOIs
StatePublished - Oct 28 2022
Event2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022 - Nashville, United States
Duration: Oct 31 2022Nov 4 2022

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Number1
Volume14
ISSN (Print)2325-0178

Conference

Conference2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022
Country/TerritoryUnited States
CityNashville
Period10/31/2211/4/22

Funding

This paper was made possible through funding from the U.S. Department of Energy’s Office of Nuclear Energy under the Nuclear Energy Enabling Technologies Program. We are grateful to Melissa Bates and Daniel Nichols at the U.S. Department of Energy, and to Pattrick Calderoni at Idaho National Laboratory for championing this effort. The data used in this research were provided by Constellation (previously known as Exelon Generation Company). We are also thankful to the plant engineers for their technical discussions on the data. The project team extends thanks to Richard V. Vilim at Argonne National Laboratory for his technical support and review of the reports, presentations, and other relevant documents during the project’s period of performance.

FundersFunder number
U.S. Department of Energy
Office of Nuclear Energy

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