CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity
Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection fra...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Dan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 35(2023), 22 vom: 28. Apr., Seite 16403-16427 |
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| Übergeordnetes Werk: |
volume:35 ; year:2023 ; number:22 ; day:28 ; month:04 ; pages:16403-16427 |
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| DOI / URN: |
10.1007/s00521-023-08507-y |
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SPR052221156 |
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| 520 | |a Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. | ||
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| 650 | 4 | |a Convolutional auto-encoder |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a The Siamese network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Threshold selection |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhong, Shisheng |0 (orcid)0000-0001-5622-9953 |4 aut | |
| 700 | 1 | |a Lin, Lin |4 aut | |
| 700 | 1 | |a Zhao, Minghang |4 aut | |
| 700 | 1 | |a Fu, Xuyun |4 aut | |
| 700 | 1 | |a Liu, Xueyun |4 aut | |
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10.1007/s00521-023-08507-y doi (DE-627)SPR052221156 (SPR)s00521-023-08507-y-e DE-627 ger DE-627 rakwb eng Liu, Dan verfasserin aut CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. Anomaly detection (dpeaa)DE-He213 Convolutional auto-encoder (dpeaa)DE-He213 The Siamese network (dpeaa)DE-He213 Threshold selection (dpeaa)DE-He213 Zhong, Shisheng (orcid)0000-0001-5622-9953 aut Lin, Lin aut Zhao, Minghang aut Fu, Xuyun aut Liu, Xueyun aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 22 vom: 28. Apr., Seite 16403-16427 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:22 day:28 month:04 pages:16403-16427 https://dx.doi.org/10.1007/s00521-023-08507-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 22 28 04 16403-16427 |
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10.1007/s00521-023-08507-y doi (DE-627)SPR052221156 (SPR)s00521-023-08507-y-e DE-627 ger DE-627 rakwb eng Liu, Dan verfasserin aut CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. Anomaly detection (dpeaa)DE-He213 Convolutional auto-encoder (dpeaa)DE-He213 The Siamese network (dpeaa)DE-He213 Threshold selection (dpeaa)DE-He213 Zhong, Shisheng (orcid)0000-0001-5622-9953 aut Lin, Lin aut Zhao, Minghang aut Fu, Xuyun aut Liu, Xueyun aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 22 vom: 28. Apr., Seite 16403-16427 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:22 day:28 month:04 pages:16403-16427 https://dx.doi.org/10.1007/s00521-023-08507-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 22 28 04 16403-16427 |
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10.1007/s00521-023-08507-y doi (DE-627)SPR052221156 (SPR)s00521-023-08507-y-e DE-627 ger DE-627 rakwb eng Liu, Dan verfasserin aut CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. Anomaly detection (dpeaa)DE-He213 Convolutional auto-encoder (dpeaa)DE-He213 The Siamese network (dpeaa)DE-He213 Threshold selection (dpeaa)DE-He213 Zhong, Shisheng (orcid)0000-0001-5622-9953 aut Lin, Lin aut Zhao, Minghang aut Fu, Xuyun aut Liu, Xueyun aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 22 vom: 28. Apr., Seite 16403-16427 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:22 day:28 month:04 pages:16403-16427 https://dx.doi.org/10.1007/s00521-023-08507-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 22 28 04 16403-16427 |
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10.1007/s00521-023-08507-y doi (DE-627)SPR052221156 (SPR)s00521-023-08507-y-e DE-627 ger DE-627 rakwb eng Liu, Dan verfasserin aut CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. Anomaly detection (dpeaa)DE-He213 Convolutional auto-encoder (dpeaa)DE-He213 The Siamese network (dpeaa)DE-He213 Threshold selection (dpeaa)DE-He213 Zhong, Shisheng (orcid)0000-0001-5622-9953 aut Lin, Lin aut Zhao, Minghang aut Fu, Xuyun aut Liu, Xueyun aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 22 vom: 28. Apr., Seite 16403-16427 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:22 day:28 month:04 pages:16403-16427 https://dx.doi.org/10.1007/s00521-023-08507-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 22 28 04 16403-16427 |
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10.1007/s00521-023-08507-y doi (DE-627)SPR052221156 (SPR)s00521-023-08507-y-e DE-627 ger DE-627 rakwb eng Liu, Dan verfasserin aut CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. Anomaly detection (dpeaa)DE-He213 Convolutional auto-encoder (dpeaa)DE-He213 The Siamese network (dpeaa)DE-He213 Threshold selection (dpeaa)DE-He213 Zhong, Shisheng (orcid)0000-0001-5622-9953 aut Lin, Lin aut Zhao, Minghang aut Fu, Xuyun aut Liu, Xueyun aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 22 vom: 28. Apr., Seite 16403-16427 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:22 day:28 month:04 pages:16403-16427 https://dx.doi.org/10.1007/s00521-023-08507-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 22 28 04 16403-16427 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. 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csiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity |
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CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity |
| abstract |
Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The main problem in data-driven anomaly detection of gas turbines is that the monitoring data consists of only a very small number of abnormal samples with the overwhelming majority of normal samples. To address this problem, this paper develops a novel semi-supervised anomaly detection framework, namely CSiamese, and the parameters of the framework are optimized only using normal samples, with the ultimate purpose of improving the anomaly detection performance on imbalanced data sets. First, the convolutional auto-encoder is used to learn the reconstructed representation of the input sample. Second, the Siamese network is selected to learn to measure the similarity between the input and its reconstructed representation under noise conditions. Besides, a new loss function is developed by improving the contrastive loss, namely triangle loss, and can reduce the risk of collapsing solutions of the Siamese network when only using positive sample pairs. Third, maximum likelihood estimation is used to set the proper detection threshold to separate abnormal samples from normal samples. Finally, the effectiveness of the developed CSiamese has been evaluated using the real monitoring data of gas turbines and a public CIFAR-10 data set. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity |
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https://dx.doi.org/10.1007/s00521-023-08507-y |
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Zhong, Shisheng Lin, Lin Zhao, Minghang Fu, Xuyun Liu, Xueyun |
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Zhong, Shisheng Lin, Lin Zhao, Minghang Fu, Xuyun Liu, Xueyun |
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10.1007/s00521-023-08507-y |
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2024-07-04T01:51:28.368Z |
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| score |
7.399663 |