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
Autor*in: |
Shi, Huaitao [verfasserIn] |
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Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Rechteinformationen: |
Nutzungsrecht: © 2014 Taylor & Francis 2014 |
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Systematik: |
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Übergeordnetes Werk: |
Enthalten in: International journal of systems science - London [u.a.] : Taylor & Francis, 1970, 47(2016), 5, Seite 1095-1109 |
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Übergeordnetes Werk: |
volume:47 ; year:2016 ; number:5 ; pages:1095-1109 |
Links: |
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DOI / URN: |
10.1080/00207721.2014.912780 |
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Katalog-ID: |
OLC1975253183 |
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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 | |
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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 |
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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 |
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Enthalten in International journal of systems science 47(2016), 5, Seite 1095-1109 volume:47 year:2016 number:5 pages:1095-1109 |
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Shi, Huaitao @@aut@@ Liu, Jianchang @@oth@@ Wu, Yuhou @@oth@@ Zhang, Ke @@oth@@ Zhang, Lixiu @@oth@@ Xue, Peng @@oth@@ |
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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. 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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 |
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fault diagnosis of nonlinear and large-scale processes using novel modified kernel fisher discriminant analysis approach |
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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. |
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Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach |
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