| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 7563091 | Chemometrics and Intelligent Laboratory Systems | 2015 | 27 Pages |
Abstract
Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this parameter is here applied in multivariate time series analysis to achieve an improved diagnosis of process dynamics. To this end, OPLS-VIP has been tested in three real-world industrial data sets; the first data set corresponds to a pulp manufacturing process using a continuous digester, the second one involves data from an industrial heater that experienced problems, and the third data set contains measures of the chemical oxygen demand into the effluent of a newsprint mill. The outcomes obtained using OPLS-VIP are benchmarked against classical PLS-VIP results. It is demonstrated how OPLS-VIP provides a better diagnosis and understanding of the time series behavior than PLS-VIP.
Keywords
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Beatriz Galindo-Prieto, Lennart Eriksson, Johan Trygg,