title: A KPI-based process monitoring and fault detection framework for large-scale processes.
abstract: Large-scale processes, consisting of multiple interconnected subprocesses, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel representation of each subprocess, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods.
authors.affiliation: Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany. Electronic address: kai.zhang@uni-due.de.
authors.forename: Kai
authors.initials: K
authors.lastname: Zhang
authors.affiliation: Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany; Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, E6-3006, 200 University Ave. W., Waterloo, ON, Canada N2L 3G1. Electronic address: yuri.shardt@uni-due.de.
authors.forename: Yuri A W
authors.initials: YAW
authors.lastname: Shardt
authors.affiliation: Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany. Electronic address: zhiwen.chen@uni-due.de.
authors.forename: Zhiwen
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Key Laboratory for Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology of Beijing, 100083 Beijing, PR China. Electronic address: xu.yang@ustb.edu.cn.
authors.forename: Xu
authors.initials: X
authors.lastname: Yang
authors.affiliation: Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Bismarckstrasse 81 BB, 47057 Duisburg, Germany. Electronic address: steven.ding@uni-due.de.
authors.forename: Steven X
authors.initials: SX
authors.lastname: Ding
authors.affiliation: Key Laboratory for Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology of Beijing, 100083 Beijing, PR China. Electronic address: kaixiang@ustb.edu.cn.
authors.forename: Kaixiang
authors.initials: K
authors.lastname: Peng
keywords: Data-driven subspace; KPI; Large-scale; Least square; Process monitoring