Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace
Nonlinear faults are difficultly separated for amounts of redundancy process variables in process industry. This paper introduces an improved kernel fisher distinguish analysis method (KFDA). All the original process variables with faults are firstly optimally classified in multi-KFDA (MKFDA) subspa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/729763 |
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| author | Ying-ying Su Shan Liang Jing-zhe Li Xiao-gang Deng Tai-fu Li Cheng Zeng |
| author_facet | Ying-ying Su Shan Liang Jing-zhe Li Xiao-gang Deng Tai-fu Li Cheng Zeng |
| author_sort | Ying-ying Su |
| collection | DOAJ |
| description | Nonlinear faults are difficultly separated for amounts of redundancy process variables in process industry. This paper introduces an improved kernel fisher distinguish analysis method (KFDA). All the original process variables with faults are firstly optimally classified in multi-KFDA (MKFDA) subspace to obtain fisher criterion values. Multikernel is used to consider different distributions for variables. Then each variable is eliminated once from original sets, and new projection is computed with the same MKFDA direction. From this, differences between new Fisher criterion values and the original ones are tested. If it changed obviously, the effect of eliminated variable should be much important on faults called false nearest neighbors (FNN). The same test is applied to the remaining variables in turn. Two nonlinear faults crossed in Tennessee Eastman process are separated with lower observation variables for further study. Results show that the method in the paper can eliminate redundant and irrelevant nonlinear process variables as well as enhancing the accuracy of classification. |
| format | Article |
| id | doaj-art-797c3365bbfd45cf9a0c0339af09ecf2 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-797c3365bbfd45cf9a0c0339af09ecf22025-08-20T03:35:23ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/729763729763Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA SubspaceYing-ying Su0Shan Liang1Jing-zhe Li2Xiao-gang Deng3Tai-fu Li4Cheng Zeng5College of Automation, Chongqing University, Chongqing 400044, ChinaCollege of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Safety Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaCollege of Mechanical and Power Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaSchool of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaCollege of Automation, Chongqing University, Chongqing 400044, ChinaNonlinear faults are difficultly separated for amounts of redundancy process variables in process industry. This paper introduces an improved kernel fisher distinguish analysis method (KFDA). All the original process variables with faults are firstly optimally classified in multi-KFDA (MKFDA) subspace to obtain fisher criterion values. Multikernel is used to consider different distributions for variables. Then each variable is eliminated once from original sets, and new projection is computed with the same MKFDA direction. From this, differences between new Fisher criterion values and the original ones are tested. If it changed obviously, the effect of eliminated variable should be much important on faults called false nearest neighbors (FNN). The same test is applied to the remaining variables in turn. Two nonlinear faults crossed in Tennessee Eastman process are separated with lower observation variables for further study. Results show that the method in the paper can eliminate redundant and irrelevant nonlinear process variables as well as enhancing the accuracy of classification.http://dx.doi.org/10.1155/2014/729763 |
| spellingShingle | Ying-ying Su Shan Liang Jing-zhe Li Xiao-gang Deng Tai-fu Li Cheng Zeng Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace Journal of Applied Mathematics |
| title | Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace |
| title_full | Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace |
| title_fullStr | Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace |
| title_full_unstemmed | Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace |
| title_short | Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace |
| title_sort | nonlinear fault separation for redundancy process variables based on fnn in mkfda subspace |
| url | http://dx.doi.org/10.1155/2014/729763 |
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