文献类型：期刊文献

英文题名：Adaptive Selective Ensemble-Independent Component Analysis Models for Process Monitoring

作者：Li, Zhichao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2018

卷号：57

期号：24

起止页码：8240

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20182305287534);WOS:【SCI-EXPANDED(收录号:WOS:000436380600013)】；

基金：The authors are grateful for the support of the Fundamental Research Funds for the Central Universities under Grant of China (222201717006).

语种：英文

外文关键词：Inference engines - Bayesian networks - Learning systems - Wastewater treatment - Process control - Process monitoring

摘要：Independent component analysis (ICA) has been widely used in non-Gaussian industrial process monitoring. However, the stability of performance and determination of dominant ICs are still the main problems for ICA. Constructing a monitoring model to achieve the best performance for different faults is a great challenge owing to the diversity and unknowability of faults. This study develops an adaptive selective ensemble ICA models method to improve the monitoring performance. Ensemble learning based on the bagging algorithm is adopted to enhance the stability of ICA. According to the difference of ICs selected for ICA modeling and hierarchical clustering, corresponding model sets are constructed for each sample subset. To ensure the accuracy of each submodel, an adaptive method based on just-in-time learning is proposed to model selection. Bayesian inference is applied to determine the final monitoring index. The validity of the proposed approach is attested through a numerical example, TE benchmark process, and wastewater treatment plants.