文献类型：期刊文献

英文题名：Fault-Relevant Optimal Ensemble ICA Model for Non-Gaussian 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：2020

卷号：28

期号：6

起止页码：2581

外文期刊名：IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

收录：;EI(收录号：20204309372565);WOS:【SCI-EXPANDED(收录号:WOS:000579414800042)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 21878081 and in part by the Fundamental Research Funds for the Central Universities of China under Grant 222201717006.

语种：英文

外文关键词：Monitoring; Adaptation models; Statistics; Integrated circuit modeling; Sociology; Data models; Ensemble learning; independent component analysis (ICA); multi-objective optimization; process monitoring; wastewater treatment plant (WWTP)

摘要：Independent component analysis (ICA) has been extensively used in non-Gaussian process monitoring. Most ICA-based monitoring methods usually establish a single monitoring model based on a certain dimension reduction criteria. However, the fault information is complex and changeable in actual industrial processes, causing the weak generalization ability of the single monitoring model. From the perspective of ensemble learning, we use the normal and available fault samples to establish several base models and ensemble them to improve the generalization ability of the monitoring model. First, the fastICA algorithm is used to extract all independent components based on the normal data. Then, a multi-objective optimization (NSGA-II) is adopted to optimize the two conditions (accuracy and diversity) of ensemble learning techniques to obtain an optimal set of base models. Finally, the monitoring results of the base models are fused through Bayesian inference. The proposed method can significantly improve the monitoring performance and the generalization ability for process monitoring. Case studies on the TE benchmark process and the wastewater treatment plant illustrate the validity and benefits of the proposed approach. In addition, we also analyzed the effects on the generalization ability of the ensemble model when the number of faults available is different.