Title :
Fault detection for chemical process based on robust PLS
Author :
Hu Yi ; Ma Hehe ; Shi Hongbo
Author_Institution :
Key Lab. of Adv. Control & Optimization for Chem. Processes of Minist. of Educ., East China Univ. of Sci. & Technol., Shanghai, China
Abstract :
The training dataset collected from industrial processes usually contain some outliers, and partial least squares (PLS) regression will have poor properties due to the sensitiveness of PLS to outliers. Under this circumstance, a multivariate statistical process monitoring method based on robust PLS (RPLS) is developed. By means of weight strategy, RPLS can eliminate the effects of the outliers in the original data and construct precise model. Then, robust monitoring statistics and control limits are derived for process monitoring purposes. A case study of the Tennessee Eastman (TE) process illustrated that the proposed approach showed superior process monitoring performance compared to conventional PLS when the modeling data set contains outliers.
Keywords :
chemical industry; fault diagnosis; least squares approximations; manufacturing processes; process monitoring; regression analysis; PLS regression; TE process; Tennessee Eastman process; chemical process; control limit; fault detection; multivariate statistical process monitoring method; partial least squares regression; weight strategy; Chemical processes; Data models; Electronic mail; Fault detection; Monitoring; Process control; Robustness; Fault detection; Outliers; Partial least squares; Robust partial least square;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561830
