Locality Preserving Discriminative Canonical Variate Analysis for Fault Diagnosis
Name
2018J4_Lu_CCE.pdf
Description
Accepted version
Size
2.16 MB
Format
Adobe PDF
Checksum (MD5)
82add92afb34a4acf1b51cb7bc56daf6
Author(s)
Lu, Qiugang
Jiang, Benben
Gopaluni, R Bhushan
Loewen, Philip D
Braatz, Richard D
Date Issued
2018
Journal
Computers and Chemical Engineering
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2018 Elsevier Ltd This paper proposes a locality preserving discriminative canonical variate analysis (LP-DCVA) scheme for fault diagnosis. The LP-DCVA method provides a set of optimal projection vectors that simultaneously maximizes the within-class mutual canonical correlations, minimizes the between-class mutual canonical correlations, and preserves the local structures present in the data. This method inherits the strength of canonical variate analysis (CVA) in handling high-dimensional data with serial correlations and the advantages of Fisher discriminant analysis (FDA) in pattern classification. Moreover, the incorporation of locality preserving projection (LPP) in this method makes it suitable for dealing with nonlinearities in the form of local manifolds in the data. The solution to the proposed approach is formulated as a generalized eigenvalue problem. The effectiveness of the proposed approach for fault classification is verified by the Tennessee Eastman process. Simulation results show that the LP-DCVA method outperforms the FDA, dynamic FDA (DFDA), CVA-FDA, and localized DFDA (L-DFDA) approaches in fault diagnosis.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.COMPCHEMENG.2018.06.017
