Modeling and Monitoring Between-Mode Transition of Multimodes Processes

被引:58
作者
Zhang, Yingwei [1 ]
Li, Shuai [1 ]
机构
[1] Northeastern Univ, State Lab Synth Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China
关键词
Manifold; multimodes processes modeling; monitoring; between-mode transition; subspace separation; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL-ANALYSIS; NONLINEAR PROCESSES; FAULT-DETECTION; IDENTIFICATION; KPCA;
D O I
10.1109/TII.2012.2220977
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The electro-fused magnesia furnace (EFMF) has complex characteristics, such as strong nonlinearity and multimodes. In this paper, the between-mode process modeling and monitoring method of the EFMF is proposed. In the original methods, the data are handled in a single mode matrices, the influence from one mode to another tends to be ignored. However, the hidden effect could be useful in process analysis and control. New method is proposed for between-mode part to establish an integrated monitoring system, which would simplify the monitoring model structure and enhance its robustness. The manifold is learned to extract the common part of between-mode transition and the monitoring performance of the between-mode is significantly improved. From the between-mode viewpoint, the multimodes processes behaviors are separated into two subspaces. In the common subspace, the underlying process-relevant variation stays invariable, showing the common contribution to multimodes processes. The specific subspace changes with the alternation of modes and has the different influences on multimodes processes modeling and monitoring. Based on subspace separation, process information is captured across modes and between-mode transition regions are distinguished from two modes. Two modes and between-mode transition models are developed respectively for multimodes processes monitoring. Experiment results show effectiveness of the proposed method.
引用
收藏
页码:2248 / 2255
页数:8
相关论文
共 27 条
[1]   Reconstruction-based contribution for process monitoring [J].
Alcala, Carlos F. ;
Qin, S. Joe .
AUTOMATICA, 2009, 45 (07) :1593-1600
[2]   Multi-phase principal component analysis for batch processes modelling [J].
Camacho, J ;
Picó, J .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2006, 81 (02) :127-136
[3]   Multi-phase analysis framework for handling batch process data [J].
Camacho, Jose ;
Pico, Jesus ;
Ferrer, Alberto .
JOURNAL OF CHEMOMETRICS, 2008, 22 (11-12) :632-643
[4]   Fault identification for process monitoring using kernel principal component analysis [J].
Cho, JH ;
Lee, JM ;
Choi, SW ;
Lee, D ;
Lee, IB .
CHEMICAL ENGINEERING SCIENCE, 2005, 60 (01) :279-288
[5]   Nonlinear multiscale modelling for fault detection and identification [J].
Choi, Sang Wook ;
Morris, Julian ;
Lee, In-Beum .
CHEMICAL ENGINEERING SCIENCE, 2008, 63 (08) :2252-2266
[6]   Improving Fault Tolerance in High-Precision Clock Synchronization [J].
Gaderer, Georg ;
Loschmidt, Patrick ;
Sauter, Thilo .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2010, 6 (02) :206-215
[7]   High-gain estimator and fault-tolerant design with application to a gas turbine dynamic system [J].
Gao, Zhiwei ;
Breikin, Timofei ;
Wang, Hong .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2007, 15 (04) :740-753
[8]   Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine [J].
Gao, Zhiwei ;
Dai, Xuewu ;
Breikin, Tim ;
Wang, Hong .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2008, 4 (04) :271-279
[9]   Improved kernel PCA-based monitoring approach for nonlinear processes [J].
Ge, Zhiqiang ;
Yang, Chunjie ;
Song, Zhihuan .
CHEMICAL ENGINEERING SCIENCE, 2009, 64 (09) :2245-2255
[10]   Modelling of multi-block data [J].
Hoskuldsson, Agnar ;
Svinning, Ketil .
JOURNAL OF CHEMOMETRICS, 2006, 20 (8-10) :376-385