Multiscale classification and its application to process monitoring  

作　　者：Yu-ming LIU Lu-bin YE Ping-you ZHENG Xiang-rong SHI Bin HU Jun LIANG 

机构地区：[1]Institute of Industrial Control, State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2010年第6期425-434,共10页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (No. 60574047);the National High-Tech R & D Program (863) of China (Nos. 2007AA04Z168 and 2009AA04Z154);the Research Fund for the Doctoral Program of Higher Education in China (No. 20050335018)

摘　　要：Multiscale classification has potential advantages for monitoring industrial processes generally driven by events in different time and frequency domains.In this study, we adopt stationary wavelet transform for multiscale analysis and propose an applicable scale selection method to obtain the most discriminative scale features.Then using the multiscale features, we construct two classifiers:(1) a supported vector machine(SVM) classifier based on classification distance, and(2) a Bayes classifier based on probability estimation.For the SVM classifier, we use 4-fold cross-validation and grid-search to obtain the optimal parameters.For the Bayes classifier, we introduce dimension reduction techniques including kernel Fisher discriminant analysis(KFDA) and principal component analysis(PCA) to investigate their influence on classification accuracy.We tested the classifiers with two simulated benchmark processes:the continuous stirred tank reactor(CSTR) process and the Tennessee Eastman(TE) process.We also tested them on a real polypropylene production process.The performance comparison among the classifiers in different scales and scale combinations showed that when datasets present typical scale features, the multiscale classifier had higher classification accuracy than conventional single scale classifiers.We also found that dimension reduction can generally contribute to a better classification in our tests.Multiscale classification has potential advantages for monitoring industrial processes generally driven by events in different time and frequency domains. In this study, we adopt stationary wavelet transform for multiscale analysis and propose an applicable scale selection method to obtain the most discriminative scale features. Then using the multiscale features, we construct two classifiers： （1） a supported vector machine （SVM） classifier based on classification distance, and （2） a Bayes classifier based on probability estimation. For the SVM classifier, we use 4-fold cross-validation and grid-search to obtain the optimal parameters. For the Bayes classifier, we introduce dimension reduction techniques including kernel Fisher discriminant analysis （KFDA） and principal component analysis （PCA） to investigate their influence on classification accuracy. We tested the classifiers with two simulated benchmark processes： the continuous stirred tank reactor （CSTR） process and the Tennessee Eastman （TE） process. We also tested them on a real polypropylene production process. The performance comparison among the classifiers in different scales and scale combinations showed that when datasets present typical scale features, the multiscale classifier had higher classification accuracy than conventional single scale classifiers. We also found that dimension reduction can generally contribute to a better classification in our tests.

关 键 词：Multiscale analysis Stationary wavelet transform Multi-class classifier Feature extraction Process monitoring 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]