Non-gaussian batch process monitoring based on MWSVDD of similarity measure

doi:10.3969/j.issn.1001-0505.2019.02.009

	Non-gaussian batch process monitoring based on MWSVDD of similarity measure
	Zhao, Xiaoqiang 1,2,3; W., Zhou
刊名	Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition)
	2019-03-20
卷号	49 期号:2 页码:259-266
关键词	Bayesian networks Data description Gaussian distribution Gaussian noise (electronic) Inference engines Principal component analysis Process control Process monitoring Testing Vector spaces Batch process Mixture distributions Multiphase Similarity measure Support vector data description
ISSN号	10010505
DOI	10.3969/j.issn.1001-0505.2019.02.009
英文摘要	Aiming at nonlinearity, multiphase and the Gaussian and non-Gaussian mixture distribution of process variables in batch processes, a multiway weighted support vector data description algorithm based on similarity measure MWSVDD(SmMWSVDD) was proposed in this paper. Firstly, the algorithm divided the multiphase process into a stable phase and a transitional phase by considering the similarity between phases. Then, a new kernel similarity weight was defined in high dimensional kernel space to balance all the radiuses obtained by support vector data description (SVDD) modeling, overcoming the shortcoming of the control limits constructed by SVDD. The mixture distribution was divided into Gaussian distribution and non-Gaussian distribution variables by a D-test method to be modeled and monitored using multiway kernel principal component analysis (MKPCA) and improved SVDD. Finally, the integration unified monitoring statistic was built at each phase by Bayesian inference and verified by the penicillin fermentation process. The result shows that the proposed algorithm can reduce the false alarm rate by 20.21% and the missed alarm rate by 10.27% on average than MKPCA and SVDD. Thus, it is more effective for multiphase and mixture distributional batch process monitoring. © 2019, Editorial Department of Journal of Southeast University. All right reserved.
语种	中文
出版者	Southeast University
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/113944]
专题	电气工程与信息工程学院
作者单位	1.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou; 730050, China; 2.College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 3.National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Zhao, Xiaoqiang,W., Zhou. Non-gaussian batch process monitoring based on MWSVDD of similarity measure[J]. Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition),2019,49(2):259-266.
APA	Zhao, Xiaoqiang,&W., Zhou.(2019).Non-gaussian batch process monitoring based on MWSVDD of similarity measure.Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition),49(2),259-266.
MLA	Zhao, Xiaoqiang,et al."Non-gaussian batch process monitoring based on MWSVDD of similarity measure".Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) 49.2(2019):259-266.