TY - JOUR
T1 - An embedded fault detection, isolation and accommodation system in a model predictive controller for an industrial benchmark process
AU - Kettunen, Markus
AU - Zhang, P.
AU - Jämsä-Jounela, Sirkka-Liisa
PY - 2008
Y1 - 2008
N2 - Fault detection and isolation (FDI) for industrial processes has been actively studied during the last decades. Traditionally, the most widely implemented FDI methods have been based on model-based approaches. In modern process industry, however, there is a demand for data-based methods due to the complexity and limited availability of the mechanistic models. The aim of this paper is to present a data-based, fault tolerant control (FTC) system for a simulated industrial benchmark process, Shell control problem. Data-based FDI systems, employing principal component analysis (PCA), partial least squares (PLS) and subspace model identification (SMI) are presented for achieving fault tolerance in cooperation with controllers. The effectiveness of the methods is tested by introducing faults in simulated process measurements. The process is controlled by using model predictive control (MPC). To compare the effectiveness of the MPC, the FTC system is also tested with a control strategy based on a set of PI controllers.
AB - Fault detection and isolation (FDI) for industrial processes has been actively studied during the last decades. Traditionally, the most widely implemented FDI methods have been based on model-based approaches. In modern process industry, however, there is a demand for data-based methods due to the complexity and limited availability of the mechanistic models. The aim of this paper is to present a data-based, fault tolerant control (FTC) system for a simulated industrial benchmark process, Shell control problem. Data-based FDI systems, employing principal component analysis (PCA), partial least squares (PLS) and subspace model identification (SMI) are presented for achieving fault tolerance in cooperation with controllers. The effectiveness of the methods is tested by introducing faults in simulated process measurements. The process is controlled by using model predictive control (MPC). To compare the effectiveness of the MPC, the FTC system is also tested with a control strategy based on a set of PI controllers.
KW - FTC, MPC, PCA, PLS,Shell control problem,SMI
KW - FTC, MPC, PCA, PLS,Shell control problem,SMI
KW - FTC
KW - PCA
KW - PLS
KW - SMI
KW - MPC
KW - Shell control problem
UR - http://dx.doi.org/10.1016/j.compchemeng.2008.03.011
M3 - Article
VL - 32
SP - 2966
EP - 2985
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
SN - 0098-1354
IS - 12
ER -