基于MKECA的非高斯性和非线性共存的间歇过程监测

doi:10.11949/j.issn.0438-1157.20171329

化工学报 ›› 2018, Vol. 69 ›› Issue (3): 1200-1206.DOI: 10.11949/j.issn.0438-1157.20171329

基于MKECA的非高斯性和非线性共存的间歇过程监测

常鹏^1,2, 乔俊飞^1,2, 王普^1,2, 高学金^1,2, 李征^1,2

1 北京工业大学信息学部, 北京 100124;
2 数字社区教育部工程研究中心, 北京 100124

收稿日期:2017-10-09 修回日期:2017-10-19 出版日期:2018-03-05 发布日期:2018-03-05
通讯作者: 常鹏
基金资助:
国家自然科学基金项目（61640312）；北京市自然科学基金项目（4172007）；北京博士后工作经费资助项目。

Monitoring non-Gaussian and non-linear batch process based on multi-way kernel entropy component analysis

CHANG Peng^1,2, QIAO Junfei^1,2, WANG Pu^1,2, GAO Xuejin^1,2, LI Zheng^1,2

1 Department of Information, Beijing University of Technology, Beijing 100124, China;
2 Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China

Received:2017-10-09 Revised:2017-10-19 Online:2018-03-05 Published:2018-03-05
Supported by:
supported by the National Natural Science Foundation of China (61640312),the Beijing Natural Science Foundation (4172007) and the Beijing Postdoctoral Research Foundation.

摘要/Abstract

摘要：

多向核独立成分分析（multiway kernel independent component analysis，MKICA）在监测间歇过程非高斯性和非线性方面取得了广泛应用，其仅仅是将线性独立成分分析（independent component analysis，ICA）方法利用核主成分分析（kernel principal component analysis，KPCA）白化扩展到非线性领域，但数据经KPCA白化后只考虑数据信息最大化未考虑数据簇结构信息的不足，为解决此问题，采用核熵成分分析（kernel entropy component analysis，KECA）代替KPCA白化的过程监测方法。该方法首先利用AT展开方法将过程三维数据变为二维数据；其次用KECA进行白化处理的同时解决数据的非线性；然后建立ICA监测模型用于非高斯生产过程监测；最后将该方法应用到青霉素发酵仿真和实际的工业过程并与MKICA方法进行对比，验证该方法的有效性。

关键词: 间歇过程, 多向核独立成分分析, 多向核熵成分分析, 多向核熵独立成分分析

Abstract:

Multi-kernel independent component analysis (MKICA) has been widely used in monitoring non-Gaussian and non-linear processes. The technique uses only non-linear extension of linear independent component analysis (ICA) by KPCA data whitening. After KPCA data whitening, the data is considered only to maximize data information but not data cluster structure information. In order to solve this problem, kernel entropy component analysis (kernel entropy component analysis, KECA) was proposed to replace KPCA whitening in process monitoring. First, 3D data is transformed into 2D data by AT expansion. Second, data nonlinearity was resolved during KECA whitening. Third, ICA monitoring model was established for non-Gaussian production process monitoring. The method was applied to simulation and actual industrial process of Penicillin fermentation, which showed effectiveness of the method in comparison with the MKICA method.

Key words: batch process, multiway kernel independent component analysis, multiway kernel entropy component analysis, multiway kernel entropy independent component

中图分类号:

TP277

常鹏, 乔俊飞, 王普, 高学金, 李征. 基于MKECA的非高斯性和非线性共存的间歇过程监测[J]. 化工学报, 2018, 69(3): 1200-1206.

CHANG Peng, QIAO Junfei, WANG Pu, GAO Xuejin, LI Zheng. Monitoring non-Gaussian and non-linear batch process based on multi-way kernel entropy component analysis[J]. CIESC Journal, 2018, 69(3): 1200-1206.

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基于MKECA的非高斯性和非线性共存的间歇过程监测

Monitoring non-Gaussian and non-linear batch process based on multi-way kernel entropy component analysis

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