Direct projection to latent variable space for fault detection

Partial least squares (PLSs) often require many latent variables (LVs) T to describe the variations in process variables X correlated with quality variables Y, which are obtained via the traditional nonlinear iterative PLS (NIPALS) optimal solution based on (X, Y). Total projection to latent structu...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2014-03, Vol.351 (3), p.1226-1250
Main Authors: Hu, Jing, Wen, Chenglin, Li, Ping, Yuan, Tianqi
Format: Article
Language:English
Subjects:
Computer simulation
Correlation
Fault detection
Least squares method
Mathematical models
Optimization
Projection
Statistical analysis
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Summary:Partial least squares (PLSs) often require many latent variables (LVs) T to describe the variations in process variables X correlated with quality variables Y, which are obtained via the traditional nonlinear iterative PLS (NIPALS) optimal solution based on (X, Y). Total projection to latent structures (T-PLSs) performs further decomposition to extract LVs Ty directly related to Y from T, which are obtained by the PCA optimal solution based on the predicted value of Y. Inspired by T-PLS, combined with practical process characteristics, two fault detection approaches are proposed in this paper to solve problems encountered by T-PLS. Without the NIPALS, (X, Y) are projected into the latent variable space determined by main variations of Y directly. Furthermore, the structure and characteristics of several modified methods in statistical analysis are studied based on calculation procedures of solving PCA, PLS and T-PLS optimization problems, and the geometric significance of the T-PLS model is demonstrated in detail. Simulation analysis and case studies both indicate the effectiveness of the proposed approaches.
ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2013.10.007