Plant-wide process monitoring based on multiblock MICA-PCA

被引：0

作者：

Wang Z.-L. ^{[1
]}

Jiang W. ^{[1
]}

Wang X. ^{[2
]}

机构：

[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai

[2] Center of Electrical & Electronic Technology, Shanghai Jiaotong University, Shanghai

来源：

Wang, Zhen-Lei (wangzhen_1@ecust.edu.cn) | 2018年 / Northeast University卷 / 33期

关键词：

Multiblock; Non-Gaussian; Plant-wide process; Principal component analysis;

D O I：

10.13195/j.kzyjc.2016.1222

中图分类号：

学科分类号：

摘要：

The multiblock strategy is widely used for plant-wide process monitoring, to capture the relations between complex process variables of the plant-wide process, however, the sub-block data obtained from the traditional multiblock method still has the problem of non-Gaussian and Gaussian mixture distribution, which influences the performance of process monitoring. Therefore, a plant-wide process monitoring method based on multiblock MICA-PCA is proposed to improve the process monitoring performance. Firstly, the measured variables are automatically divided into non-Gaussian block and Gaussian block through the Jarque-Bera(J-B) test method, the non-Gaussian block and Gaussian block are divided into non-Gaussian sub-blocks and Gaussian sub-blocks through the Hellinger Distance(HD) method. By using different modeling and diagnosis methods for non-Gaussian sub-blocks and Gaussian sub-blocks, the monitoring effect is improved. Finally, the proposed method is applied to monitor the Tennessee-Eastman(TE) process, which shows its effectiveness. © 2018, Editorial Office of Control and Decision. All right reserved.

引用

页码：269 / 274

页数：5

共 19 条

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