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Comprehensive monitoring of industrial processes using multivariable characteristics evaluation and subspace decomposition
Li S(李帅)1,2,3,4; Zhou XF(周晓锋)1,2,3; Shi HB(史海波)1,2,3; Pan FC(潘福成)1,2,3; Li X(李歆)1,2,3; Zhang YC(张宜弛)1,2,3
Department数字工厂研究室
Source PublicationCANADIAN JOURNAL OF CHEMICAL ENGINEERING
ISSN0008-4034
2021
Pages1-18
Indexed BySCI ; EI
EI Accession number20213510839007
WOS IDWOS:000691014400001
Contribution Rank1
Funding OrganizationNatural Science Foundation of Liaoning Province, ChinaNatural Science Foundation of Liaoning Province [2019-MS-344]
Keywordcomprehensive monitoring fault detection industrial processes multivariable characteristics subspace decomposition
Abstract

Gaussianity, non-Gaussianity, linearity, and nonlinearity generally coexist within industrial process variables, and should be taken into account simultaneously for process modelling with monitoring. This paper presents a comprehensive monitoring method of industrial processes using multivariable characteristics evaluation and subspace decomposition. First, a multivariable characteristics evaluation method is presented to divide the process variables into the Gaussian linear, Gaussian nonlinear, non-Gaussian linear, and non-Gaussian nonlinear subspaces. Second, the PCA-ICA-KPCA-KICA-based multivariable subspace decomposition is proposed for process modelling. Furthermore, comprehensive monitoring is developed and final results are combined using comprehensive statistics. By multivariable characteristics evaluation and subspace decomposition, the proposed method could evaluate and seek the multivariable characteristics and enhance the performance of process monitoring. The effectiveness and feasibility of the proposed comprehensive monitoring method are demonstrated by a numerical system and the benchmark Tennessee Eastman (TE) process.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29551
Collection数字工厂研究室
Corresponding AuthorLi S(李帅); Zhou XF(周晓锋)
Affiliation1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
4.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Li S,Zhou XF,Shi HB,et al. Comprehensive monitoring of industrial processes using multivariable characteristics evaluation and subspace decomposition[J]. CANADIAN JOURNAL OF CHEMICAL ENGINEERING,2021:1-18.
APA Li S,Zhou XF,Shi HB,Pan FC,Li X,&Zhang YC.(2021).Comprehensive monitoring of industrial processes using multivariable characteristics evaluation and subspace decomposition.CANADIAN JOURNAL OF CHEMICAL ENGINEERING,1-18.
MLA Li S,et al."Comprehensive monitoring of industrial processes using multivariable characteristics evaluation and subspace decomposition".CANADIAN JOURNAL OF CHEMICAL ENGINEERING (2021):1-18.
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