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《Journal of Zhejiang University(Engineering Science)》 2006-01
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Monitoring method based on multivariate statistical signal processing for batch process

CHEN Guo-jin,LIANG Jun,LIU Yu-ming,QIAN Ji-xin(Institute of System Engineering,National Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China)  
A new method based on multivariate statistical signal processing was presented for a large number of batch process in modern chemical industry,which divides the process information space into three subspace: subspace described by blind source signals,subspace described by principal components and subspace of residual information.Then corresponding control limits or operating features classifiers were built to monitor the process.The feasibility of this method was investigated by applying it to a batch process,and the results show that this method can:1)not only detect process faults in time,but also confirm the source of faults exactly;2)extract or model process features much more simply in each subspace than in whole process information space and 3)overcome the limitation of the assumption in conventional multivariate statistical process control(MSPC) that the separated latent variables must be subjected to normal probability distribution.
【Fund】: 国家自然科学基金资助项目(60574047);; 教育部高等学校博士学科点专项基金资助项目(20050335018)
【CateGory Index】: TP277
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1 WANG Haiqing, SONG Zhihuan and WANG Hui (National Laboratory of Industrial Control Technology ,Institute of Industrial Process Control, Zhejiang University,Hangzhou 310027,Zhejiang,China);FAULT DETECTION BEHAVIOR ANALYSIS OF PCA-BASED PROCESS MONITORING APPROACH[J];Journal of Chemical Industry and Engineering(China);2002-03
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