[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]
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基于相似性度量的MWSVDD非高斯间歇过程监控()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
49
期数:
2019年第2期
页码:
259-266
栏目:
自动化
出版日期:
2019-03-20

文章信息/Info

Title:
Non-gaussian batch process monitoring based on MWSVDD of similarity measure
作者:
赵小强123周文伟1
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):
Zhao Xiaoqiang123 Zhou Wenwei1
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
关键词:
间歇过程 多阶段 混合分布 支持向量数据描述 相似性度量
Keywords:
batch process multiphase mixture distribution support vector data description similarity measure
分类号:
TP277
DOI:
10.3969/j.issn.1001-0505.2019.02.009
摘要:
针对间歇过程的非线性、多阶段、过程变量的高斯和非高斯混合分布问题,提出了基于相似性度量的多向加权支持向量数据描述(similarity measure-MWSVDD, SmMWSVDD)算法.该算法首先考虑了阶段间的相似性,将多阶段过程分为稳定阶段和过渡阶段;然后在高维核空间定义了一种新的核相似度权重,对支持向量数据描述(SVDD)建模得到的所有半径进行均衡考虑,克服了SVDD构建控制限的缺陷;通过D检验法将混合分布变量分为高斯分布和非高斯分布变量,并分别用多向核主成分分析(MKPCA)和改进的SVDD进行建模监控;最后通过贝叶斯推断在每个阶段集成各自统一的监控量.通过青霉素发酵实验平台进行验证,结果表明,所提算法比MKPCA和SVDD算法的误报率平均降低了20.21%和漏报率平均降低了10.27%,对多阶段和混合分布的间歇过程监控更加有效.
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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备注/Memo

备注/Memo:
收稿日期: 2018-09-05.
作者简介: 赵小强(1969—),男,博士,教授,博士生导师,xqzhao@lut.cn.
基金项目: 国家自然科学基金资助项目(61763029)、甘肃省基础研究创新群体基金资助项目(1506RJIA031).
引用本文: 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.
更新日期/Last Update: 2019-03-20