Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods
In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry compet...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.696785/full |
| _version_ | 1819104509693001728 |
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| author | Xingang Zhao Junyung Kim Kyle Warns Xinyan Wang Pradeep Ramuhalli Sacit Cetiner Hyun Gook Kang Michael Golay |
| author_facet | Xingang Zhao Junyung Kim Kyle Warns Xinyan Wang Pradeep Ramuhalli Sacit Cetiner Hyun Gook Kang Michael Golay |
| author_sort | Xingang Zhao |
| collection | DOAJ |
| description | In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification. |
| first_indexed | 2024-12-22T02:07:29Z |
| format | Article |
| id | doaj.art-2d06d57649a649899414a974395e0b93 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-22T02:07:29Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-2d06d57649a649899414a974395e0b932022-12-21T18:42:30ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-06-01910.3389/fenrg.2021.696785696785Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven MethodsXingang Zhao0Junyung Kim1Kyle Warns2Xinyan Wang3Pradeep Ramuhalli4Sacit Cetiner5Hyun Gook Kang6Michael Golay7Nuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesDepartment of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, United StatesDepartment of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, United StatesDepartment of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, United StatesNuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesNuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesDepartment of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, United StatesDepartment of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, United StatesIn a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.https://www.frontiersin.org/articles/10.3389/fenrg.2021.696785/fullprognostics and health managementplanning and decision-makingcondition-based maintenanceartificial intelligencemachine learningdata-driven methods |
| spellingShingle | Xingang Zhao Junyung Kim Kyle Warns Xinyan Wang Pradeep Ramuhalli Sacit Cetiner Hyun Gook Kang Michael Golay Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods Frontiers in Energy Research prognostics and health management planning and decision-making condition-based maintenance artificial intelligence machine learning data-driven methods |
| title | Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods |
| title_full | Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods |
| title_fullStr | Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods |
| title_full_unstemmed | Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods |
| title_short | Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods |
| title_sort | prognostics and health management in nuclear power plants an updated method centric review with special focus on data driven methods |
| topic | prognostics and health management planning and decision-making condition-based maintenance artificial intelligence machine learning data-driven methods |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.696785/full |
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