Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/590163
Title: Fault Diagnosis Method of Joint Fisher Discriminant Analysis Based on the Local and Global Manifold Learning and Its Kernel Version
Authors: Jian Feng;Jian Wang;Huaguang Zhang;Zhiyan Han
subject: Data-driven modeling;manifold learning;feature extraction;Fisher discirminant analysis (FDA);fault diagnosis;kernel method
Year: 2016
Publisher: IEEE
Abstract: Though Fisher discriminant analysis (FDA) is an outstanding method of fault diagnosis, it is usually difficult to extract the discriminant information in a complex industrial environment. One of the reasons is that, in such an environment, the discriminant information can not been extracted entirely due to the disturbances, non-Gaussianity and nonlinearity. In this paper, a method named Joint Fisher discriminant analysis (JFDA) is proposed to address the issues. First, JFDA removes outliers caused by disturbances according to the energy density of each datum. Then, for the non-Gaussianity and weakly nonlinearity, the novel scatter matrices are defined to extract both of the local and global discriminant information based on the manifold learning. Finally, the kernel JFDA (KJFDA) is investigated to hold the manifold assumption because the strongly nonlinearity may weaken the assumption and cause overlapping. The proposed method is applied to the Tennessee Eastman process (TEP). The results demonstrate that KJFDA shows a better performance of fault diagnosis than other improved versions of FDA.
URI: http://localhost/handle/Hannan/173134
http://localhost/handle/Hannan/590163
ISSN: 1545-5955
1558-3783
volume: 13
issue: 1
Appears in Collections:2016

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Title: Fault Diagnosis Method of Joint Fisher Discriminant Analysis Based on the Local and Global Manifold Learning and Its Kernel Version
Authors: Jian Feng;Jian Wang;Huaguang Zhang;Zhiyan Han
subject: Data-driven modeling;manifold learning;feature extraction;Fisher discirminant analysis (FDA);fault diagnosis;kernel method
Year: 2016
Publisher: IEEE
Abstract: Though Fisher discriminant analysis (FDA) is an outstanding method of fault diagnosis, it is usually difficult to extract the discriminant information in a complex industrial environment. One of the reasons is that, in such an environment, the discriminant information can not been extracted entirely due to the disturbances, non-Gaussianity and nonlinearity. In this paper, a method named Joint Fisher discriminant analysis (JFDA) is proposed to address the issues. First, JFDA removes outliers caused by disturbances according to the energy density of each datum. Then, for the non-Gaussianity and weakly nonlinearity, the novel scatter matrices are defined to extract both of the local and global discriminant information based on the manifold learning. Finally, the kernel JFDA (KJFDA) is investigated to hold the manifold assumption because the strongly nonlinearity may weaken the assumption and cause overlapping. The proposed method is applied to the Tennessee Eastman process (TEP). The results demonstrate that KJFDA shows a better performance of fault diagnosis than other improved versions of FDA.
URI: http://localhost/handle/Hannan/173134
http://localhost/handle/Hannan/590163
ISSN: 1545-5955
1558-3783
volume: 13
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7091038.pdf2.35 MBAdobe PDFThumbnail
Preview File
Title: Fault Diagnosis Method of Joint Fisher Discriminant Analysis Based on the Local and Global Manifold Learning and Its Kernel Version
Authors: Jian Feng;Jian Wang;Huaguang Zhang;Zhiyan Han
subject: Data-driven modeling;manifold learning;feature extraction;Fisher discirminant analysis (FDA);fault diagnosis;kernel method
Year: 2016
Publisher: IEEE
Abstract: Though Fisher discriminant analysis (FDA) is an outstanding method of fault diagnosis, it is usually difficult to extract the discriminant information in a complex industrial environment. One of the reasons is that, in such an environment, the discriminant information can not been extracted entirely due to the disturbances, non-Gaussianity and nonlinearity. In this paper, a method named Joint Fisher discriminant analysis (JFDA) is proposed to address the issues. First, JFDA removes outliers caused by disturbances according to the energy density of each datum. Then, for the non-Gaussianity and weakly nonlinearity, the novel scatter matrices are defined to extract both of the local and global discriminant information based on the manifold learning. Finally, the kernel JFDA (KJFDA) is investigated to hold the manifold assumption because the strongly nonlinearity may weaken the assumption and cause overlapping. The proposed method is applied to the Tennessee Eastman process (TEP). The results demonstrate that KJFDA shows a better performance of fault diagnosis than other improved versions of FDA.
URI: http://localhost/handle/Hannan/173134
http://localhost/handle/Hannan/590163
ISSN: 1545-5955
1558-3783
volume: 13
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7091038.pdf2.35 MBAdobe PDFThumbnail
Preview File