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Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (6): 425-434    DOI: 10.1631/jzus.C0910430
    
Multiscale classification and its application to process monitoring
Yu-ming Liu, Lu-bin Ye, Ping-you Zheng, Xiang-rong Shi, Bin Hu, Jun Liang*
Institute of Industrial Control, State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
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Abstract  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.

Key wordsMultiscale analysis      Stationary wavelet transform      Multi-class classifier      Feature extraction      Process monitoring     
Received: 15 July 2009      Published: 02 June 2010
CLC:  TP277  
Fund:  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),  and  the  Re-search Fund for the Doctoral Program of Higher Education in China
(No. 20050335018)
Cite this article:

Yu-ming Liu, Lu-bin Ye, Ping-you Zheng, Xiang-rong Shi, Bin Hu, Jun Liang. Multiscale classification and its application to process monitoring. Front. Inform. Technol. Electron. Eng., 2010, 11(6): 425-434.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.C0910430     OR     http://www.zjujournals.com/xueshu/fitee/Y2010/V11/I6/425


Multiscale classification and its application to process monitoring

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 
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