Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach

It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisa...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Shi, Huaitao [verfasserIn] Liu, Jianchang Wu, Yuhou Zhang, Ke Zhang, Lixiu Xue, Peng

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Rechteinformationen:	Nutzungsrecht: © 2014 Taylor & Francis 2014

Schlagwörter:	feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation

Systematik:	SA 5880

Übergeordnetes Werk:	Enthalten in: International journal of systems science - London [u.a.] : Taylor & Francis, 1970, 47(2016), 5, Seite 1095-1109
Übergeordnetes Werk:	volume:47 ; year:2016 ; number:5 ; pages:1095-1109

Links:	Volltext Link aufrufen

DOI / URN:	10.1080/00207721.2014.912780

Katalog-ID:	OLC1975253183

Internformat


LEADER	01000caa a2200265 4500
001	OLC1975253183
003	DE-627
005	20220220112355.0
007	tu
008	160609s2016 xx \|\|\|\|\| 00\| \|\|eng c
024	7		\|a 10.1080/00207721.2014.912780 \|2 doi
028	5	2	\|a PQ20160610
035			\|a (DE-627)OLC1975253183
035			\|a (DE-599)GBVOLC1975253183
035			\|a (PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10
035			\|a (KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 510 \|a 000 \|a 620 \|q DNB
084			\|a SA 5880 \|q AVZ \|2 rvk
084			\|a 30.10 \|2 bkl
084			\|a 31.80 \|2 bkl
084			\|a 54.76 \|2 bkl
100	1		\|a Shi, Huaitao \|e verfasserin \|4 aut
245	1	0	\|a Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach
264		1	\|c 2016
336			\|a Text \|b txt \|2 rdacontent
337			\|a ohne Hilfsmittel zu benutzen \|b n \|2 rdamedia
338			\|a Band \|b nc \|2 rdacarrier
520			\|a It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.
540			\|a Nutzungsrecht: © 2014 Taylor & Francis 2014
650		4	\|a feature selection
650		4	\|a fault diagnosis
650		4	\|a kernel Fisher discriminant analysis
650		4	\|a kernel parameter optimisation
650		4	\|a improved biogeography-based optimisation
700	1		\|a Liu, Jianchang \|4 oth
700	1		\|a Wu, Yuhou \|4 oth
700	1		\|a Zhang, Ke \|4 oth
700	1		\|a Zhang, Lixiu \|4 oth
700	1		\|a Xue, Peng \|4 oth
773	0	8	\|i Enthalten in \|t International journal of systems science \|d London [u.a.] : Taylor & Francis, 1970 \|g 47(2016), 5, Seite 1095-1109 \|w (DE-627)129358843 \|w (DE-600)160479-X \|w (DE-576)014731169 \|x 0020-7721 \|7 nnns
773	1	8	\|g volume:47 \|g year:2016 \|g number:5 \|g pages:1095-1109
856	4	1	\|u http://dx.doi.org/10.1080/00207721.2014.912780 \|3 Volltext
856	4	2	\|u http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_OLC
912			\|a SSG-OLC-TEC
912			\|a SSG-OLC-MAT
912			\|a SSG-OPC-MAT
912			\|a GBV_ILN_70
936	r	v	\|a SA 5880
936	b	k	\|a 30.10 \|q AVZ
936	b	k	\|a 31.80 \|q AVZ
936	b	k	\|a 54.76 \|q AVZ
951			\|a AR
952			\|d 47 \|j 2016 \|e 5 \|h 1095-1109

Indexfelder

author_variant	h s hs
matchkey_str	article:00207721:2016----::aldanssfolnaadagsaercseuigoemdfekrefsed
hierarchy_sort_str	2016
bklnumber	30.10 31.80 54.76
publishDate	2016
allfields	10.1080/00207721.2014.912780 doi PQ20160610 (DE-627)OLC1975253183 (DE-599)GBVOLC1975253183 (PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10 (KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi DE-627 ger DE-627 rakwb eng 510 000 620 DNB SA 5880 AVZ rvk 30.10 bkl 31.80 bkl 54.76 bkl Shi, Huaitao verfasserin aut Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity. Nutzungsrecht: © 2014 Taylor & Francis 2014 feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation Liu, Jianchang oth Wu, Yuhou oth Zhang, Ke oth Zhang, Lixiu oth Xue, Peng oth Enthalten in International journal of systems science London [u.a.] : Taylor & Francis, 1970 47(2016), 5, Seite 1095-1109 (DE-627)129358843 (DE-600)160479-X (DE-576)014731169 0020-7721 nnns volume:47 year:2016 number:5 pages:1095-1109 http://dx.doi.org/10.1080/00207721.2014.912780 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 SA 5880 30.10 AVZ 31.80 AVZ 54.76 AVZ AR 47 2016 5 1095-1109
spelling	10.1080/00207721.2014.912780 doi PQ20160610 (DE-627)OLC1975253183 (DE-599)GBVOLC1975253183 (PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10 (KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi DE-627 ger DE-627 rakwb eng 510 000 620 DNB SA 5880 AVZ rvk 30.10 bkl 31.80 bkl 54.76 bkl Shi, Huaitao verfasserin aut Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity. Nutzungsrecht: © 2014 Taylor & Francis 2014 feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation Liu, Jianchang oth Wu, Yuhou oth Zhang, Ke oth Zhang, Lixiu oth Xue, Peng oth Enthalten in International journal of systems science London [u.a.] : Taylor & Francis, 1970 47(2016), 5, Seite 1095-1109 (DE-627)129358843 (DE-600)160479-X (DE-576)014731169 0020-7721 nnns volume:47 year:2016 number:5 pages:1095-1109 http://dx.doi.org/10.1080/00207721.2014.912780 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 SA 5880 30.10 AVZ 31.80 AVZ 54.76 AVZ AR 47 2016 5 1095-1109
allfields_unstemmed	10.1080/00207721.2014.912780 doi PQ20160610 (DE-627)OLC1975253183 (DE-599)GBVOLC1975253183 (PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10 (KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi DE-627 ger DE-627 rakwb eng 510 000 620 DNB SA 5880 AVZ rvk 30.10 bkl 31.80 bkl 54.76 bkl Shi, Huaitao verfasserin aut Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity. Nutzungsrecht: © 2014 Taylor & Francis 2014 feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation Liu, Jianchang oth Wu, Yuhou oth Zhang, Ke oth Zhang, Lixiu oth Xue, Peng oth Enthalten in International journal of systems science London [u.a.] : Taylor & Francis, 1970 47(2016), 5, Seite 1095-1109 (DE-627)129358843 (DE-600)160479-X (DE-576)014731169 0020-7721 nnns volume:47 year:2016 number:5 pages:1095-1109 http://dx.doi.org/10.1080/00207721.2014.912780 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 SA 5880 30.10 AVZ 31.80 AVZ 54.76 AVZ AR 47 2016 5 1095-1109
allfieldsGer	10.1080/00207721.2014.912780 doi PQ20160610 (DE-627)OLC1975253183 (DE-599)GBVOLC1975253183 (PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10 (KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi DE-627 ger DE-627 rakwb eng 510 000 620 DNB SA 5880 AVZ rvk 30.10 bkl 31.80 bkl 54.76 bkl Shi, Huaitao verfasserin aut Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity. Nutzungsrecht: © 2014 Taylor & Francis 2014 feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation Liu, Jianchang oth Wu, Yuhou oth Zhang, Ke oth Zhang, Lixiu oth Xue, Peng oth Enthalten in International journal of systems science London [u.a.] : Taylor & Francis, 1970 47(2016), 5, Seite 1095-1109 (DE-627)129358843 (DE-600)160479-X (DE-576)014731169 0020-7721 nnns volume:47 year:2016 number:5 pages:1095-1109 http://dx.doi.org/10.1080/00207721.2014.912780 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 SA 5880 30.10 AVZ 31.80 AVZ 54.76 AVZ AR 47 2016 5 1095-1109
allfieldsSound	10.1080/00207721.2014.912780 doi PQ20160610 (DE-627)OLC1975253183 (DE-599)GBVOLC1975253183 (PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10 (KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi DE-627 ger DE-627 rakwb eng 510 000 620 DNB SA 5880 AVZ rvk 30.10 bkl 31.80 bkl 54.76 bkl Shi, Huaitao verfasserin aut Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity. Nutzungsrecht: © 2014 Taylor & Francis 2014 feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation Liu, Jianchang oth Wu, Yuhou oth Zhang, Ke oth Zhang, Lixiu oth Xue, Peng oth Enthalten in International journal of systems science London [u.a.] : Taylor & Francis, 1970 47(2016), 5, Seite 1095-1109 (DE-627)129358843 (DE-600)160479-X (DE-576)014731169 0020-7721 nnns volume:47 year:2016 number:5 pages:1095-1109 http://dx.doi.org/10.1080/00207721.2014.912780 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 SA 5880 30.10 AVZ 31.80 AVZ 54.76 AVZ AR 47 2016 5 1095-1109
language	English
source	Enthalten in International journal of systems science 47(2016), 5, Seite 1095-1109 volume:47 year:2016 number:5 pages:1095-1109
sourceStr	Enthalten in International journal of systems science 47(2016), 5, Seite 1095-1109 volume:47 year:2016 number:5 pages:1095-1109
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation
dewey-raw	510
isfreeaccess_bool	false
container_title	International journal of systems science
authorswithroles_txt_mv	Shi, Huaitao @@aut@@ Liu, Jianchang @@oth@@ Wu, Yuhou @@oth@@ Zhang, Ke @@oth@@ Zhang, Lixiu @@oth@@ Xue, Peng @@oth@@
publishDateDaySort_date	2016-01-01T00:00:00Z
hierarchy_top_id	129358843
dewey-sort	3510
id	OLC1975253183
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1975253183</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220220112355.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160609s2016 xx \|\|\|\|\| 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00207721.2014.912780</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160610</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1975253183</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1975253183</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="a">000</subfield><subfield code="a">620</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 5880</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">30.10</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.80</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Shi, Huaitao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2014 Taylor & Francis 2014</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">kernel Fisher discriminant analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">kernel parameter optimisation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">improved biogeography-based optimisation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Jianchang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Yuhou</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Ke</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Lixiu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xue, Peng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of systems science</subfield><subfield code="d">London [u.a.] : Taylor & Francis, 1970</subfield><subfield code="g">47(2016), 5, Seite 1095-1109</subfield><subfield code="w">(DE-627)129358843</subfield><subfield code="w">(DE-600)160479-X</subfield><subfield code="w">(DE-576)014731169</subfield><subfield code="x">0020-7721</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:47</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:1095-1109</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00207721.2014.912780</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 5880</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">30.10</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.80</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.76</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">47</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="h">1095-1109</subfield></datafield></record></collection>
author	Shi, Huaitao
spellingShingle	Shi, Huaitao ddc 510 rvk SA 5880 bkl 30.10 bkl 31.80 bkl 54.76 misc feature selection misc fault diagnosis misc kernel Fisher discriminant analysis misc kernel parameter optimisation misc improved biogeography-based optimisation Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach
authorStr	Shi, Huaitao
ppnlink_with_tag_str_mv	@@773@@(DE-627)129358843
format	Article
dewey-ones	510 - Mathematics 000 - Computer science, information & general works 620 - Engineering & allied operations
delete_txt_mv	keep
author_role	aut
collection	OLC
remote_str	false
illustrated	Not Illustrated
issn	0020-7721
topic_title	510 000 620 DNB SA 5880 AVZ rvk 30.10 bkl 31.80 bkl 54.76 bkl Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach feature selection fault diagnosis kernel Fisher discriminant analysis kernel parameter optimisation improved biogeography-based optimisation
topic	ddc 510 rvk SA 5880 bkl 30.10 bkl 31.80 bkl 54.76 misc feature selection misc fault diagnosis misc kernel Fisher discriminant analysis misc kernel parameter optimisation misc improved biogeography-based optimisation
topic_unstemmed	ddc 510 rvk SA 5880 bkl 30.10 bkl 31.80 bkl 54.76 misc feature selection misc fault diagnosis misc kernel Fisher discriminant analysis misc kernel parameter optimisation misc improved biogeography-based optimisation
topic_browse	ddc 510 rvk SA 5880 bkl 30.10 bkl 31.80 bkl 54.76 misc feature selection misc fault diagnosis misc kernel Fisher discriminant analysis misc kernel parameter optimisation misc improved biogeography-based optimisation
format_facet	Aufsätze Gedruckte Aufsätze
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	nc
author2_variant	j l jl y w yw k z kz l z lz p x px
hierarchy_parent_title	International journal of systems science
hierarchy_parent_id	129358843
dewey-tens	510 - Mathematics 000 - Computer science, knowledge & systems 620 - Engineering
hierarchy_top_title	International journal of systems science
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)129358843 (DE-600)160479-X (DE-576)014731169
title	Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach
ctrlnum	(DE-627)OLC1975253183 (DE-599)GBVOLC1975253183 (PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10 (KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi
title_full	Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach
author_sort	Shi, Huaitao
journal	International journal of systems science
journalStr	International journal of systems science
lang_code	eng
isOA_bool	false
dewey-hundreds	500 - Science 000 - Computer science, information & general works 600 - Technology
recordtype	marc
publishDateSort	2016
contenttype_str_mv	txt
container_start_page	1095
author_browse	Shi, Huaitao
container_volume	47
class	510 000 620 DNB SA 5880 AVZ rvk 30.10 bkl 31.80 bkl 54.76 bkl
format_se	Aufsätze
author-letter	Shi, Huaitao
doi_str_mv	10.1080/00207721.2014.912780
dewey-full	510 000 620
title_sort	fault diagnosis of nonlinear and large-scale processes using novel modified kernel fisher discriminant analysis approach
title_auth	Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach
abstract	It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.
abstractGer	It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.
abstract_unstemmed	It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70
container_issue	5
title_short	Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach
url	http://dx.doi.org/10.1080/00207721.2014.912780 http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780
remote_bool	false
author2	Liu, Jianchang Wu, Yuhou Zhang, Ke Zhang, Lixiu Xue, Peng
author2Str	Liu, Jianchang Wu, Yuhou Zhang, Ke Zhang, Lixiu Xue, Peng
ppnlink	129358843
mediatype_str_mv	n
isOA_txt	false
hochschulschrift_bool	false
author2_role	oth oth oth oth oth
doi_str	10.1080/00207721.2014.912780
up_date	2024-07-04T06:08:03.420Z
_version_	1803627563223875584
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1975253183</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220220112355.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160609s2016 xx \|\|\|\|\| 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00207721.2014.912780</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160610</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1975253183</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1975253183</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1889-fec8e5adfb19d4ae0cad9387abea88204cfbc9e7f683a86fff1de7fcea114ec10</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0076963020160000047000501095faultdiagnosisofnonlinearandlargescaleprocessesusi</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="a">000</subfield><subfield code="a">620</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 5880</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">30.10</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.80</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Shi, Huaitao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2014 Taylor & Francis 2014</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">kernel Fisher discriminant analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">kernel parameter optimisation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">improved biogeography-based optimisation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Jianchang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Yuhou</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Ke</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Lixiu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xue, Peng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of systems science</subfield><subfield code="d">London [u.a.] : Taylor & Francis, 1970</subfield><subfield code="g">47(2016), 5, Seite 1095-1109</subfield><subfield code="w">(DE-627)129358843</subfield><subfield code="w">(DE-600)160479-X</subfield><subfield code="w">(DE-576)014731169</subfield><subfield code="x">0020-7721</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:47</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:1095-1109</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00207721.2014.912780</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00207721.2014.912780</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 5880</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">30.10</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.80</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.76</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">47</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="h">1095-1109</subfield></datafield></record></collection>
score	7.4006987

Nicht das Richtige dabei?

Schreiben Sie uns!

Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?