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CLC number: TP277

On-line Access: 2010-06-02

Received: 2009-07-15

Revision Accepted: 2009-10-26

Crosschecked: 2010-05-04

Cited: 6

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.6 P.425-434


Multiscale classification and its application to process monitoring

Author(s):  Yu-ming Liu, Lu-bin Ye, Ping-you Zheng, Xiang-rong Shi, Bin Hu, Jun Liang

Affiliation(s):  Institute of Industrial Control, State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   jliang@iipc.zju.edu.cn

Key Words:  Multiscale analysis, Stationary wavelet transform, Multi-class classifier, Feature extraction, Process monitoring

Yu-ming Liu, Lu-bin Ye, Ping-you Zheng, Xiang-rong Shi, Bin Hu, Jun Liang. Multiscale classification and its application to process monitoring[J]. Journal of Zhejiang University Science C, 2010, 11(6): 425-434.

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author="Yu-ming Liu, Lu-bin Ye, Ping-you Zheng, Xiang-rong Shi, Bin Hu, Jun Liang",
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%T Multiscale classification and its application to process monitoring
%A Yu-ming Liu
%A Lu-bin Ye
%A Ping-you Zheng
%A Xiang-rong Shi
%A Bin Hu
%A Jun Liang
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%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910430

T1 - Multiscale classification and its application to process monitoring
A1 - Yu-ming Liu
A1 - Lu-bin Ye
A1 - Ping-you Zheng
A1 - Xiang-rong Shi
A1 - Bin Hu
A1 - Jun Liang
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 6
SP - 425
EP - 434
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C0910430

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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