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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.<\/jats:p>","DOI":"10.1155\/2014\/729763","type":"journal-article","created":{"date-parts":[[2014,2,4]],"date-time":"2014-02-04T03:36:33Z","timestamp":1391484993000},"page":"1-9","source":"Crossref","is-referenced-by-count":1,"title":["Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace"],"prefix":"10.1155","volume":"2014","author":[{"given":"Ying-ying","family":"Su","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"},{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}]},{"given":"Shan","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, 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