Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM
Joint Authors
Xu, Jie
Zhao, Jin
Ma, Baoping
Hu, Shousong
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-11
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA) and sparse support vector machine (SVM).
The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA).
The data are firstly mapped into high-dimensional feature subspace.
Then, the ICA algorithm seeks the projection directions in the KPCA whitened space.
Performance monitoring is implemented through constructing the statistical index and control limit in the feature space.
If the statistical indexes exceed the predefined control limit, a fault may have occurred.
Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults.
The proposed method is applied to the simulation of Tennessee Eastman (TE) chemical process.
The simulation results show that the proposed method can identify various types of faults accurately and rapidly.
American Psychological Association (APA)
Xu, Jie& Zhao, Jin& Ma, Baoping& Hu, Shousong. 2013. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1011397
Modern Language Association (MLA)
Xu, Jie…[et al.]. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM. Mathematical Problems in Engineering No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-1011397
American Medical Association (AMA)
Xu, Jie& Zhao, Jin& Ma, Baoping& Hu, Shousong. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1011397
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1011397