Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring

Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality prese...
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Gespeichert in:

Autor*in:	Wu, Ping [verfasserIn] Zhang, Xujie [verfasserIn] He, Jiajun [verfasserIn] Lou, Siwei [verfasserIn] Gao, Jinfeng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring

Übergeordnetes Werk:	Enthalten in: Process safety and environmental protection - Amsterdam : Elsevier, 1990, 147, Seite 1088-1100
Übergeordnetes Werk:	volume:147 ; pages:1088-1100

DOI / URN:	10.1016/j.psep.2021.01.028

Katalog-ID:	ELV005617987

Internformat


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100	1		\|a Wu, Ping \|e verfasserin \|0 (orcid)0000-0002-2729-9669 \|4 aut
245	1	0	\|a Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring
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520			\|a Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods.
650		4	\|a Canonical correlation analysis
650		4	\|a Random Fourier feature map
650		4	\|a Local geometric structure information
650		4	\|a Kernel methods
650		4	\|a Real-time process monitoring
700	1		\|a Zhang, Xujie \|e verfasserin \|4 aut
700	1		\|a He, Jiajun \|e verfasserin \|4 aut
700	1		\|a Lou, Siwei \|e verfasserin \|4 aut
700	1		\|a Gao, Jinfeng \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Process safety and environmental protection \|d Amsterdam : Elsevier, 1990 \|g 147, Seite 1088-1100 \|h Online-Ressource \|w (DE-627)318710420 \|w (DE-600)2008004-9 \|w (DE-576)284747785 \|7 nnns
773	1	8	\|g volume:147 \|g pages:1088-1100
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912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
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912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
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912			\|a GBV_ILN_4313
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912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 58.18 \|j Chemische Betriebstechnik
951			\|a AR
952			\|d 147 \|h 1088-1100

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publishDate	2021
allfields	10.1016/j.psep.2021.01.028 doi (DE-627)ELV005617987 (ELSEVIER)S0957-5820(21)00037-9 DE-627 ger DE-627 rda eng 660 540 333.7 DE-600 58.18 bkl Wu, Ping verfasserin (orcid)0000-0002-2729-9669 aut Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods. Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring Zhang, Xujie verfasserin aut He, Jiajun verfasserin aut Lou, Siwei verfasserin aut Gao, Jinfeng verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 147, Seite 1088-1100 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:147 pages:1088-1100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 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_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.18 Chemische Betriebstechnik AR 147 1088-1100
spelling	10.1016/j.psep.2021.01.028 doi (DE-627)ELV005617987 (ELSEVIER)S0957-5820(21)00037-9 DE-627 ger DE-627 rda eng 660 540 333.7 DE-600 58.18 bkl Wu, Ping verfasserin (orcid)0000-0002-2729-9669 aut Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods. Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring Zhang, Xujie verfasserin aut He, Jiajun verfasserin aut Lou, Siwei verfasserin aut Gao, Jinfeng verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 147, Seite 1088-1100 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:147 pages:1088-1100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 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_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.18 Chemische Betriebstechnik AR 147 1088-1100
allfields_unstemmed	10.1016/j.psep.2021.01.028 doi (DE-627)ELV005617987 (ELSEVIER)S0957-5820(21)00037-9 DE-627 ger DE-627 rda eng 660 540 333.7 DE-600 58.18 bkl Wu, Ping verfasserin (orcid)0000-0002-2729-9669 aut Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods. Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring Zhang, Xujie verfasserin aut He, Jiajun verfasserin aut Lou, Siwei verfasserin aut Gao, Jinfeng verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 147, Seite 1088-1100 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:147 pages:1088-1100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 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_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.18 Chemische Betriebstechnik AR 147 1088-1100
allfieldsGer	10.1016/j.psep.2021.01.028 doi (DE-627)ELV005617987 (ELSEVIER)S0957-5820(21)00037-9 DE-627 ger DE-627 rda eng 660 540 333.7 DE-600 58.18 bkl Wu, Ping verfasserin (orcid)0000-0002-2729-9669 aut Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods. Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring Zhang, Xujie verfasserin aut He, Jiajun verfasserin aut Lou, Siwei verfasserin aut Gao, Jinfeng verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 147, Seite 1088-1100 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:147 pages:1088-1100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 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_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.18 Chemische Betriebstechnik AR 147 1088-1100
allfieldsSound	10.1016/j.psep.2021.01.028 doi (DE-627)ELV005617987 (ELSEVIER)S0957-5820(21)00037-9 DE-627 ger DE-627 rda eng 660 540 333.7 DE-600 58.18 bkl Wu, Ping verfasserin (orcid)0000-0002-2729-9669 aut Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods. Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring Zhang, Xujie verfasserin aut He, Jiajun verfasserin aut Lou, Siwei verfasserin aut Gao, Jinfeng verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 147, Seite 1088-1100 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:147 pages:1088-1100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 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_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.18 Chemische Betriebstechnik AR 147 1088-1100
language	English
source	Enthalten in Process safety and environmental protection 147, Seite 1088-1100 volume:147 pages:1088-1100
sourceStr	Enthalten in Process safety and environmental protection 147, Seite 1088-1100 volume:147 pages:1088-1100
format_phy_str_mv	Article
bklname	Chemische Betriebstechnik
institution	findex.gbv.de
topic_facet	Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring
dewey-raw	660
isfreeaccess_bool	false
container_title	Process safety and environmental protection
authorswithroles_txt_mv	Wu, Ping @@aut@@ Zhang, Xujie @@aut@@ He, Jiajun @@aut@@ Lou, Siwei @@aut@@ Gao, Jinfeng @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	318710420
dewey-sort	3660
id	ELV005617987
language_de	englisch
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author	Wu, Ping
spellingShingle	Wu, Ping ddc 660 bkl 58.18 misc Canonical correlation analysis misc Random Fourier feature map misc Local geometric structure information misc Kernel methods misc Real-time process monitoring Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring
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topic_title	660 540 333.7 DE-600 58.18 bkl Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring Canonical correlation analysis Random Fourier feature map Local geometric structure information Kernel methods Real-time process monitoring
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title	Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring
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title_full	Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring
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title_sort	locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring
title_auth	Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring
abstract	Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods.
abstractGer	Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods.
abstract_unstemmed	Hazard identification and analysis is an important step in the process safety assessment/management in the modern process industry. For hazard identification, real-time monitoring of process operations plays a critical role to establish required safety measures. In this paper, a novel locality preserving randomized canonical correlation analysis (LPRCCA) method is proposed for real-time nonlinear process monitoring. The basic idea is to map the original data onto a randomized low-dimensional feature space through random Fourier feature map, and then integrate the local geometric structure information to improve data mining performance. On the basis of explicit low-dimensional random Fourier features, the computational cost of the online feature extraction is dramatically reduced. The proposed LPRCCA method is significantly more favorable than kernel-based methods for nonlinear process monitoring. The applicability and effectiveness of the proposed process monitoring scheme are verified through a numerical example and an industrial benchmark of the Tennessee Eastman process (TEP) by the comparisons with other relevant methods.
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title_short	Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring
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up_date	2024-07-06T18:34:55.868Z
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Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring

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