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 language | English |
|---|---|
| Title of host publication | Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM |
| Editors | Chetan Kulkarni, Abhinav Saxena |
| Publisher | Prognostics and Health Management Society |
| Edition | 1 |
| ISBN (Electronic) | 9781936263370 |
| DOIs | |
| State | Published - Oct 28 2022 |
| Event | 2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022 - Nashville, United States Duration: Oct 31 2022 → Nov 4 2022 |
Publication series
| Name | Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM |
|---|---|
| Number | 1 |
| Volume | 14 |
| ISSN (Print) | 2325-0178 |
Conference
| Conference | 2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022 |
|---|---|
| Country/Territory | United States |
| City | Nashville |
| Period | 10/31/22 → 11/4/22 |
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.