Aging detection of plant control system components using recurrent neural network
A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe opera...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Park, JaeKwan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts - Meyers, Carina Mello Guimaraes ELSEVIER, 2022, the international review journal covering all aspects of nuclear energy, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:142 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.pnucene.2021.104005 |
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| 520 | |a A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. | ||
| 520 | |a A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. | ||
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10.1016/j.pnucene.2021.104005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001604.pica (DE-627)ELV056144490 (ELSEVIER)S0149-1970(21)00362-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.83 bkl Park, JaeKwan verfasserin aut Aging detection of plant control system components using recurrent neural network 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. Deep neural network Elsevier Aging fault Elsevier Recurrent network Elsevier Aging detection Elsevier Preventive replacement Elsevier Kim, TaekKyu oth Seong, SeungHwan oth Enthalten in Elsevier Science Meyers, Carina Mello Guimaraes ELSEVIER Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts 2022 the international review journal covering all aspects of nuclear energy Amsterdam [u.a.] (DE-627)ELV007755775 volume:142 year:2021 pages:0 https://doi.org/10.1016/j.pnucene.2021.104005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.83 Rheumatologie Orthopädie VZ AR 142 2021 0 |
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10.1016/j.pnucene.2021.104005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001604.pica (DE-627)ELV056144490 (ELSEVIER)S0149-1970(21)00362-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.83 bkl Park, JaeKwan verfasserin aut Aging detection of plant control system components using recurrent neural network 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. Deep neural network Elsevier Aging fault Elsevier Recurrent network Elsevier Aging detection Elsevier Preventive replacement Elsevier Kim, TaekKyu oth Seong, SeungHwan oth Enthalten in Elsevier Science Meyers, Carina Mello Guimaraes ELSEVIER Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts 2022 the international review journal covering all aspects of nuclear energy Amsterdam [u.a.] (DE-627)ELV007755775 volume:142 year:2021 pages:0 https://doi.org/10.1016/j.pnucene.2021.104005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.83 Rheumatologie Orthopädie VZ AR 142 2021 0 |
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10.1016/j.pnucene.2021.104005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001604.pica (DE-627)ELV056144490 (ELSEVIER)S0149-1970(21)00362-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.83 bkl Park, JaeKwan verfasserin aut Aging detection of plant control system components using recurrent neural network 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. Deep neural network Elsevier Aging fault Elsevier Recurrent network Elsevier Aging detection Elsevier Preventive replacement Elsevier Kim, TaekKyu oth Seong, SeungHwan oth Enthalten in Elsevier Science Meyers, Carina Mello Guimaraes ELSEVIER Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts 2022 the international review journal covering all aspects of nuclear energy Amsterdam [u.a.] (DE-627)ELV007755775 volume:142 year:2021 pages:0 https://doi.org/10.1016/j.pnucene.2021.104005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.83 Rheumatologie Orthopädie VZ AR 142 2021 0 |
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10.1016/j.pnucene.2021.104005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001604.pica (DE-627)ELV056144490 (ELSEVIER)S0149-1970(21)00362-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.83 bkl Park, JaeKwan verfasserin aut Aging detection of plant control system components using recurrent neural network 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. Deep neural network Elsevier Aging fault Elsevier Recurrent network Elsevier Aging detection Elsevier Preventive replacement Elsevier Kim, TaekKyu oth Seong, SeungHwan oth Enthalten in Elsevier Science Meyers, Carina Mello Guimaraes ELSEVIER Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts 2022 the international review journal covering all aspects of nuclear energy Amsterdam [u.a.] (DE-627)ELV007755775 volume:142 year:2021 pages:0 https://doi.org/10.1016/j.pnucene.2021.104005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.83 Rheumatologie Orthopädie VZ AR 142 2021 0 |
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Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts |
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Aging detection of plant control system components using recurrent neural network |
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Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts |
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aging detection of plant control system components using recurrent neural network |
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Aging detection of plant control system components using recurrent neural network |
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A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. |
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A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. |
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A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability. |
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Aging detection of plant control system components using recurrent neural network |
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