Journal of Northeastern University:Natural Science ›› 2017, Vol. 38 ›› Issue (5): 609-614.DOI: 10.12068/j.issn.1005-3026.2017.05.001

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Multivariate Process Monitoring Based on the Characteristic Analysis of the Data

ZHANG Shu-mei1, WANG Fu-li1,2, WANG Shu1,2, LI Qiang-qiang3   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; 3. College of Design & Art, Shenyang Aerospace University, Shenyang 110136, China.
  • Received:2015-12-17 Revised:2015-12-17 Online:2017-05-15 Published:2017-05-11
  • Contact: WANG Shu
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Abstract:

Multivariate statistical process monitoring methods such as PCA and ICA are always based on a variety of assumptions. If the constraints of these methods have not been considered when selecting these methods, wrong conclusions will be obtained and the rate of high leaking and false alarm will be increased. To solve the constraints problem of these methods in application, a data characteristic analysis method was proposed to test the correlation of the variables automatically, in which parameter optimization was conducted and the set of linear variables was eliminated sequentially. The simulation illustrated that the proposed method can select appropriate modelling method automatically according to data characteristics and applicable conditions of the methods, which has considerable practical value.

Key words: correlation of the variables, principal component analysis, independent component analysis, kernel principal component analysis, kernel independent component analysis

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