Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM
Electric valves have significant importance in industrial applications, especially in nuclear power plants. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. However, it is difficult to inspect each valve in convention...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2020/8349349 |
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| author | Hang Wang Min-jun Peng Yong-kuo Liu Shi-wen Liu Ren-yi Xu Hanan Saeed |
| author_facet | Hang Wang Min-jun Peng Yong-kuo Liu Shi-wen Liu Ren-yi Xu Hanan Saeed |
| author_sort | Hang Wang |
| collection | DOAJ |
| description | Electric valves have significant importance in industrial applications, especially in nuclear power plants. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. However, it is difficult to inspect each valve in conventional maintenance. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. Thus, there exists a genuine demand for remote sensing of a valve condition through nonintrusive methods as well as prediction of its remaining useful life (RUL). In this paper, typical aging modes have been summarized. The data for sensing valve conditions were gathered during aging experiments through acoustic emission sensors. During data processing, convolution kernel integrated with LSTM is utilized for feature extraction. Subsequently, LSTM which has an excellent ability in sequential analysis is used for predicting RUL. Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant. |
| format | Article |
| id | doaj-art-6208be23e6f04db7be84e2aeb1ad9dde |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-6208be23e6f04db7be84e2aeb1ad9dde2025-02-03T01:05:03ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832020-01-01202010.1155/2020/83493498349349Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTMHang Wang0Min-jun Peng1Yong-kuo Liu2Shi-wen Liu3Ren-yi Xu4Hanan Saeed5Key Subject Laboratory of Nuclear Safety and Simulation Technology, Harbin Engineering University, Harbin 150001, ChinaKey Subject Laboratory of Nuclear Safety and Simulation Technology, Harbin Engineering University, Harbin 150001, ChinaKey Subject Laboratory of Nuclear Safety and Simulation Technology, Harbin Engineering University, Harbin 150001, ChinaNuclear Power Institute of China, Chengdu 610213, ChinaKey Subject Laboratory of Nuclear Safety and Simulation Technology, Harbin Engineering University, Harbin 150001, ChinaKey Subject Laboratory of Nuclear Safety and Simulation Technology, Harbin Engineering University, Harbin 150001, ChinaElectric valves have significant importance in industrial applications, especially in nuclear power plants. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. However, it is difficult to inspect each valve in conventional maintenance. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. Thus, there exists a genuine demand for remote sensing of a valve condition through nonintrusive methods as well as prediction of its remaining useful life (RUL). In this paper, typical aging modes have been summarized. The data for sensing valve conditions were gathered during aging experiments through acoustic emission sensors. During data processing, convolution kernel integrated with LSTM is utilized for feature extraction. Subsequently, LSTM which has an excellent ability in sequential analysis is used for predicting RUL. Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.http://dx.doi.org/10.1155/2020/8349349 |
| spellingShingle | Hang Wang Min-jun Peng Yong-kuo Liu Shi-wen Liu Ren-yi Xu Hanan Saeed Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM Science and Technology of Nuclear Installations |
| title | Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM |
| title_full | Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM |
| title_fullStr | Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM |
| title_full_unstemmed | Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM |
| title_short | Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM |
| title_sort | remaining useful life prediction techniques of electric valves for nuclear power plants with convolution kernel and lstm |
| url | http://dx.doi.org/10.1155/2020/8349349 |
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