Title :
Dynamic process monitoring based on probabilistic principle component regression
Author :
Le Zhou ; Zhihuan Song ; Zhiqiang Ge ; Aimin Miao
Author_Institution :
State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
Abstract :
Probabilistic principal component analysis (PPCA) has been proposed to monitor the industrial process in the probabilistic manner. However, the traditional PPCA method is invalid when the observed data is strongly auto-correlated. For monitoring the probabilistic dynamic process, a probabilistic principal component regression (PPCR) model is proposed to extract the dynamic information of the data, based on which new monitoring statistics are constructed. The Expectation-Maximization (EM) algorithm is utilized for model training. Then, the PPCR based state space model is constructed to monitor the dynamic process. For performance evaluation, a case study on the Tennessee Eastman (TE) benchmark process is provided.
Keywords :
maximum likelihood estimation; principal component analysis; regression analysis; statistical process control; PPCA; PPCR; Tennessee Eastman benchmark process; dynamic process monitoring; expectation-maximization algorithm; industrial process; probabilistic principal component analysis; probabilistic principle component regression analysis; Data models; Monitoring; Predictive models; Principal component analysis; Probabilistic logic; Process control; Temperature measurement; EM algorithm; dynamic process monitoring; probabilistic principal component regression (PPCR);
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561795
