Nuclear power plant sensor signal reconstruction based on deep learning methods
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator...
Ausführliche Beschreibung
Autor*in: |
Yang, Zixiao [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022transfer abstract |
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Übergeordnetes Werk: |
Enthalten in: Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training - Cohen, Robbin G. ELSEVIER, 2021, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:167 ; year:2022 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.anucene.2021.108765 |
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Katalog-ID: |
ELV056339259 |
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520 | |a In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. | ||
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10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
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10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
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10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
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10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
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10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
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nuclear power plant sensor signal reconstruction based on deep learning methods |
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Nuclear power plant sensor signal reconstruction based on deep learning methods |
abstract |
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. |
abstractGer |
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. |
abstract_unstemmed |
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
title_short |
Nuclear power plant sensor signal reconstruction based on deep learning methods |
url |
https://doi.org/10.1016/j.anucene.2021.108765 |
remote_bool |
true |
author2 |
Xu, Peng Zhang, Biao Xu, Chuanlong Zhang, Liming Xie, Hongyun Duan, Qizhi |
author2Str |
Xu, Peng Zhang, Biao Xu, Chuanlong Zhang, Liming Xie, Hongyun Duan, Qizhi |
ppnlink |
ELV007956894 |
mediatype_str_mv |
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isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth oth oth |
doi_str |
10.1016/j.anucene.2021.108765 |
up_date |
2024-07-06T20:06:11.418Z |
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1803861487956000768 |
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score |
7.400139 |