Batch process monitoring based on multi-phase and multi-kernel support vector data description

被引：0

作者：

Wang X. ^{[1
,2
]}

Wang Y. ^{[1
]}

Deng X. ^{[1
]}

Cao Y. ^{[1
]}

Wang P. ^{[1
]}

机构：

[1] College of Control Science and Engineering in China University of Petroleum (East China), Qingdao

[2] College of Application Technology in Qingdao University, Qingdao

来源：

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2020年 / 44卷 / 04期

关键词：

Batch processes; Bayesian inference; Fault detection; Spectral clustering; Support vector data description;

D O I：

10.3969/j.issn.1673-5005.2020.04.021

中图分类号：

学科分类号：

摘要：

Aiming at the multi-phase characteristics and complex nonlinear characteristics of batch process data, a fault detection method using multi-phase and multi-kernel support vector data description (MPMK-SVDD) is proposed. In order to fully mine the multi-phase information of batch process data, an improved spectral clustering method based on mutual information similarity matrix is proposed to solve the problem of multi-phase partition of batch process data set. Considering the complexity and non-linearity of the process data that cannot be fully described by a single kernel function, a SVDD monitoring model based on multiple kernel functions and kernel parameters is designed, and the global monitoring statistics are constructed by Bayesian inference to detect process faults. The proposed method is verified by the simulation of penicillin fermentation process. The results show that the proposed method can detect process faults more effectively than the traditional SVDD method and has a higher fault detection rate. © 2020, Editorial Office of Journal of China University of Petroleum(Edition of Natural Science). All right reserved.

引用

页码：182 / 188

页数：6

共 18 条

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