A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection

The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this i...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fu, Song [verfasserIn] Zhong, Shisheng [verfasserIn] Lin, Lin [verfasserIn] Zhao, Minghang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine

Übergeordnetes Werk:	Enthalten in: Engineering applications of artificial intelligence - Amsterdam [u.a.] : Elsevier Science, 1988, 101
Übergeordnetes Werk:	volume:101

DOI / URN:	10.1016/j.engappai.2021.104199

Katalog-ID:	ELV005829410

Internformat


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520			\|a The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method.
650		4	\|a Re-optimized deep auto-encoder
650		4	\|a Unsupervised anomaly detection
650		4	\|a Reconstruction error
650		4	\|a Isolation forest
650		4	\|a Gas turbine
700	1		\|a Zhong, Shisheng \|e verfasserin \|4 aut
700	1		\|a Lin, Lin \|e verfasserin \|4 aut
700	1		\|a Zhao, Minghang \|e verfasserin \|4 aut
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936	b	k	\|a 50.23 \|j Regelungstechnik \|j Steuerungstechnik
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Indexfelder

author_variant	s f sf s z sz l l ll m z mz
matchkey_str	article:09521976:2021----::rotmzdepuonoefratrienuevs
hierarchy_sort_str	2021
bklnumber	50.23 54.72
publishDate	2021
allfields	10.1016/j.engappai.2021.104199 doi (DE-627)ELV005829410 (ELSEVIER)S0952-1976(21)00046-4 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Fu, Song verfasserin aut A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method. Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine Zhong, Shisheng verfasserin aut Lin, Lin verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 101 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:101 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 101
spelling	10.1016/j.engappai.2021.104199 doi (DE-627)ELV005829410 (ELSEVIER)S0952-1976(21)00046-4 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Fu, Song verfasserin aut A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method. Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine Zhong, Shisheng verfasserin aut Lin, Lin verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 101 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:101 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 101
allfields_unstemmed	10.1016/j.engappai.2021.104199 doi (DE-627)ELV005829410 (ELSEVIER)S0952-1976(21)00046-4 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Fu, Song verfasserin aut A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method. Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine Zhong, Shisheng verfasserin aut Lin, Lin verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 101 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:101 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 101
allfieldsGer	10.1016/j.engappai.2021.104199 doi (DE-627)ELV005829410 (ELSEVIER)S0952-1976(21)00046-4 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Fu, Song verfasserin aut A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method. Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine Zhong, Shisheng verfasserin aut Lin, Lin verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 101 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:101 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 101
allfieldsSound	10.1016/j.engappai.2021.104199 doi (DE-627)ELV005829410 (ELSEVIER)S0952-1976(21)00046-4 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Fu, Song verfasserin aut A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method. Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine Zhong, Shisheng verfasserin aut Lin, Lin verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 101 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:101 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 101
language	English
source	Enthalten in Engineering applications of artificial intelligence 101 volume:101
sourceStr	Enthalten in Engineering applications of artificial intelligence 101 volume:101
format_phy_str_mv	Article
bklname	Regelungstechnik Steuerungstechnik Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine
dewey-raw	004
isfreeaccess_bool	false
container_title	Engineering applications of artificial intelligence
authorswithroles_txt_mv	Fu, Song @@aut@@ Zhong, Shisheng @@aut@@ Lin, Lin @@aut@@ Zhao, Minghang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	308447832
dewey-sort	14
id	ELV005829410
language_de	englisch
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author	Fu, Song
spellingShingle	Fu, Song ddc 004 bkl 50.23 bkl 54.72 misc Re-optimized deep auto-encoder misc Unsupervised anomaly detection misc Reconstruction error misc Isolation forest misc Gas turbine A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection
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topic_title	004 DE-600 50.23 bkl 54.72 bkl A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection Re-optimized deep auto-encoder Unsupervised anomaly detection Reconstruction error Isolation forest Gas turbine
topic	ddc 004 bkl 50.23 bkl 54.72 misc Re-optimized deep auto-encoder misc Unsupervised anomaly detection misc Reconstruction error misc Isolation forest misc Gas turbine
topic_unstemmed	ddc 004 bkl 50.23 bkl 54.72 misc Re-optimized deep auto-encoder misc Unsupervised anomaly detection misc Reconstruction error misc Isolation forest misc Gas turbine
topic_browse	ddc 004 bkl 50.23 bkl 54.72 misc Re-optimized deep auto-encoder misc Unsupervised anomaly detection misc Reconstruction error misc Isolation forest misc Gas turbine
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title	A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection
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title_full	A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection
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author_browse	Fu, Song Zhong, Shisheng Lin, Lin Zhao, Minghang
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title_sort	a re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection
title_auth	A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection
abstract	The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method.
abstractGer	The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method.
abstract_unstemmed	The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method.
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title_short	A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection
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author2	Zhong, Shisheng Lin, Lin Zhao, Minghang
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up_date	2024-07-06T19:18:26.567Z
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A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection

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