期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 41, 期 -, 页码 9-17出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2014.11.003
关键词
Fault detection; Slow feature analysis; Kernel principal component analysis; Nonlinear dynamic process
类别
资金
- National Natural Science Foundation of China [61273160]
- Natural Science Foundation of Shandong Province [ZR2011FM014]
- Shandong Province Doctor Foundation [BS2012ZZ011]
A fault detection method based on dynamic kernel slow feature analysis (DKSFA) is presented in the paper. SFA is a new feature extraction technology which can find a group of slowly varying feature outputs from the high-dimensional inputs. In order to analyze the nonlinear dynamic characteristics of the process data, DKSFA is presented which applies the augmented matrix to consider the dynamic characteristic and uses kernel slow feature analysis (KSFA) to extract the nonlinear slow features hidden in the observed data. For the purpose of fault detection, the D monitoring statistic index is built based on DKSFA model and its confidence limit is computed by kernel density estimation. Simulations on a nonlinear system and Tennessee Eastman (TE) benchmark process show that the proposed method has a better fault detection performance compared with the conventional (kernel principal component analysis) KPCA-based method. (C) 2014 Elsevier Ltd. All rights reserved.
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