[1]赵小强,周文伟.基于相似性度量的MWSVDD非高斯间歇过程监控[J].东南大学学报(自然科学版),2019,49(2):259-266.[doi:10.3969/j.issn.1001-0505.2019.02.009] 　Zhao Xiaoqiang,Zhou Wenwei.Non-gaussian batch process monitoring based on MWSVDD of similarity measure[J].Journal of Southeast University (Natural Science Edition),2019,49(2):259-266.[doi:10.3969/j.issn.1001-0505.2019.02.009] 点击复制 基于相似性度量的MWSVDD非高斯间歇过程监控() 分享到： var jiathis_config = { data_track_clickback: true };

49

2019年第2期

259-266

2019-03-20

文章信息/Info

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

1兰州理工大学电气工程与信息工程学院, 兰州 730050; 2兰州理工大学甘肃省工业过程先进控制重点实验室, 兰州 730050; 3兰州理工大学国家级电气与控制工程实验教学中心, 兰州 730050
Author(s):
1College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
3National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Keywords:

TP277
DOI:
10.3969/j.issn.1001-0505.2019.02.009

Abstract:
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.

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