期刊
CONTROL ENGINEERING PRACTICE
卷 17, 期 1, 页码 221-230出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2008.07.001
关键词
Kernel dissimilarity analysis; Nonlinear processes; Distribution structure; Nonlinear kernel mapping; Kernel trick; High-dimensional feature space
资金
- National Natural Science Foundation of China [60374003, 60774068]
- Project 973 of China [2002CB312200]
To overcome the disadvantage of linear dissimilarity analysis (DISSIM) when monitoring nonlinear processes, a kernel dissimilarity analysis algorithm, termed KDISSIM here, is presented, which is the nonlinear version of DISSIM algorithm. A kernel dissimilarity index is introduced to quantitatively evaluate the differences between nonlinear data distribution structures, which can reflect the changes of nonlinear process correlations and operating conditions. In KDISSIM algorithm, the input space is first nonlinearly mapped into a high-dimensional feature space, where the initial nonlinear correlations are changed into linear ones. Then the process operating condition can be effectively tracked by investigating the linear data distributions in the feature space. The idea and effectiveness of the proposed algorithm are illustrated with respect to the simulated data collected from one typical nonlinear numerical process and the well-known Tennessee Eastman benchmark chemical process. Both the results show that the proposed method works well to Capture the underlying nonlinear process correlations thus providing a feasible and promising solution for nonlinear process monitoring. (C) 2008 Elsevier Ltd. All rights reserved.
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