Regularized error-in-variable estimation for big data modeling and process analytics

作     者：Kruger, Uwe Wang, Xun Embrechts, Mark J. Almansoori, Ali Hahn, Juergen 

作者机构：Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA Rensselaer Polytech Inst Dept Ind & Syst Engn Troy NY 12180 USA Khalifa Univ Dept Chem Engn POB 127788 Abu Dhabi U Arab Emirates 

出 版 物：《CONTROL ENGINEERING PRACTICE》 (控制工程实践)

年 卷 期：2022年第121卷第0期

页      面：105060-105060页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

主　　题：Error-in-variable models Regularization Process data analytics Parameter estimation Source signal extraction Big data 

摘      要：This article addresses estimating the uncertainty in operational data by introducing a regularized modeling technique. Existing work (i) requires knowing the true dimension of the operational data, (ii) relies on a maximum likelihood estimation that is compromised by a stringent restriction for this true dimension and (iii) is computationally expensive. In contrast, the presented regularized error-in-variable technique (i) allows determining the true data dimension through hypothesis testing, (ii) is not limited by the restriction of existing methods, and (iii) has an objective function that can be solved efficiently. Based on a simulation example and the analysis of two industrial datasets, the paper highlights that the regularized estimation technique outperforms existing work and shows how to embed this technique within an advanced process analytics framework for advanced process control, optimization and general process diagnostics.