The application of machine learning for the prognostics and health management of control element drive system
Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | Nuclear Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573319308654 |
| _version_ | 1818239467642683392 |
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| author | Adebena Oluwasegun Jae-Cheon Jung |
| author_facet | Adebena Oluwasegun Jae-Cheon Jung |
| author_sort | Adebena Oluwasegun |
| collection | DOAJ |
| description | Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability. |
| first_indexed | 2024-12-12T12:58:01Z |
| format | Article |
| id | doaj.art-8c751c99a2234bb4a824321931893344 |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-12-12T12:58:01Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-8c751c99a2234bb4a8243219318933442022-12-22T00:23:51ZengElsevierNuclear Engineering and Technology1738-57332020-10-01521022622273The application of machine learning for the prognostics and health management of control element drive systemAdebena Oluwasegun0Jae-Cheon Jung1KEPCO International Nuclear Graduate School (KINGS), 658-91 Haemaji-ro, Seosaeng-myeon, Ulju-gun, Ulsan, 45014, Republic of KoreaCorresponding author.; KEPCO International Nuclear Graduate School (KINGS), 658-91 Haemaji-ro, Seosaeng-myeon, Ulju-gun, Ulsan, 45014, Republic of KoreaDigital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.http://www.sciencedirect.com/science/article/pii/S1738573319308654Digital twinPHMMachine learningNuclear power plant |
| spellingShingle | Adebena Oluwasegun Jae-Cheon Jung The application of machine learning for the prognostics and health management of control element drive system Nuclear Engineering and Technology Digital twin PHM Machine learning Nuclear power plant |
| title | The application of machine learning for the prognostics and health management of control element drive system |
| title_full | The application of machine learning for the prognostics and health management of control element drive system |
| title_fullStr | The application of machine learning for the prognostics and health management of control element drive system |
| title_full_unstemmed | The application of machine learning for the prognostics and health management of control element drive system |
| title_short | The application of machine learning for the prognostics and health management of control element drive system |
| title_sort | application of machine learning for the prognostics and health management of control element drive system |
| topic | Digital twin PHM Machine learning Nuclear power plant |
| url | http://www.sciencedirect.com/science/article/pii/S1738573319308654 |
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