Condition Monitoring of Sensors in a NPP Using Optimized PCA
An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, incl...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2018/7689305 |
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| _version_ | 1832566734790328320 |
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| author | Wei Li Minjun Peng Yongkuo Liu Shouyu Cheng Nan Jiang Hang Wang |
| author_facet | Wei Li Minjun Peng Yongkuo Liu Shouyu Cheng Nan Jiang Hang Wang |
| author_sort | Wei Li |
| collection | DOAJ |
| description | An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method. |
| format | Article |
| id | doaj-art-127e7e7d2b9047ef9fb5e7a1cb3fadb5 |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-127e7e7d2b9047ef9fb5e7a1cb3fadb52025-02-03T01:03:16ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832018-01-01201810.1155/2018/76893057689305Condition Monitoring of Sensors in a NPP Using Optimized PCAWei Li0Minjun Peng1Yongkuo Liu2Shouyu Cheng3Nan Jiang4Hang Wang5Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaFundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaFundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaFundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaFundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaFundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaAn optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.http://dx.doi.org/10.1155/2018/7689305 |
| spellingShingle | Wei Li Minjun Peng Yongkuo Liu Shouyu Cheng Nan Jiang Hang Wang Condition Monitoring of Sensors in a NPP Using Optimized PCA Science and Technology of Nuclear Installations |
| title | Condition Monitoring of Sensors in a NPP Using Optimized PCA |
| title_full | Condition Monitoring of Sensors in a NPP Using Optimized PCA |
| title_fullStr | Condition Monitoring of Sensors in a NPP Using Optimized PCA |
| title_full_unstemmed | Condition Monitoring of Sensors in a NPP Using Optimized PCA |
| title_short | Condition Monitoring of Sensors in a NPP Using Optimized PCA |
| title_sort | condition monitoring of sensors in a npp using optimized pca |
| url | http://dx.doi.org/10.1155/2018/7689305 |
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