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《Journal of Beijing University of Chemical Technology(Natural Science Edition)》 2008-04
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An improved nonlinear principal component analysis(PCA) strategy and its application in chemical process monitoring

YANG WenJing CAO LiuLin(College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China)  
An improved nonlinear principal component analysis(NLPCA) strategy,based on an auto-associative neural network and combining the merits of wavelet analysis and independent component analysis(ICA),has been derived in order to study chemical polymerization processes.An ICA procedure was incorporated in order to capture the nonlinear characteristics with a minimum number of independent components.It was shown that multi-scale monitoring is effective in detecting different faults and leads to an increase in the reliability of process monitoring;I2,I2e and SPE statistics were subsequently constructed for fault detection.A contribution plot method was used to identify the faulty variables.The simulation results for a polyester process showed that the improved method can detect process faults earlier than traditional NLPCA,and that faulty variables can be indicated exactly by the contribution plot.
【CateGory Index】: TP277
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【Citations】
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【Co-citations】
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1 MA Chuan-yu~1,WANG Jie-sheng~2,ZHANG Yong~(1,2) (1.School of Electronic and Information Engineering,Anshan University of Science and Technology,Anshan 114044,China; 2.Dept. of Automation,Dalian University of Technology,Dalian 116023,China);Application of multivariate statistical process control on flotation process[J];Journal of Anshan University of Science and Technology;2005-Z1
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