Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace
Joint Authors
Su, Ying-ying
Li, Tai-fu
Zeng, Cheng
Deng, Xiao-gang
Li, Jing-zhe
Liang, Shan
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Su, Ying-ying& Liang, Shan& Li, Jing-zhe& Deng, Xiao-gang& Li, Tai-fu& Zeng, Cheng. 2014. Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-494056
Modern Language Association (MLA)
Su, Ying-ying…[et al.]. Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace. Journal of Applied Mathematics No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-494056
American Medical Association (AMA)
Su, Ying-ying& Liang, Shan& Li, Jing-zhe& Deng, Xiao-gang& Li, Tai-fu& Zeng, Cheng. Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-494056
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-494056