Batch process monitoring based on WGNPE-GSVDD related and independent variables

Yongyong Hui; Xiaoqiang Zhao

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Batch process monitoring based on WGNPE-GSVDD related and independent variables

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Batch process monitoring based on WGNPE-GSVDD related and independent variables
- Batch process monitoring based on WGNPE-GSVDD related and independent variables
- 中国化学工程学报（英文版） 2018年26卷第12期页码：2549-2561
- Affiliations：
  
  1. 2 Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology,Lanzhou,China,730050
  2. 3 National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology,Lanzhou,China,730050
- Author bio：
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- 纸质出版：2018
- Accepted：
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Yongyong Hui, Xiaoqiang Zhao. Batch process monitoring based on WGNPE-GSVDD related and independent variables[J]. 中国化学工程学报（英文版）, 2018,26(12):2549-2561.

Yongyong Hui, Xiaoqiang Zhao. Batch process monitoring based on WGNPE-GSVDD related and independent variables[J]. Chin.J.Chem.Eng., 2018, 26(12): 2549-2561.
Yongyong Hui, Xiaoqiang Zhao. Batch process monitoring based on WGNPE-GSVDD related and independent variables[J]. 中国化学工程学报（英文版）, 2018,26(12):2549-2561. DOI：

Yongyong Hui, Xiaoqiang Zhao. Batch process monitoring based on WGNPE-GSVDD related and independent variables[J]. Chin.J.Chem.Eng., 2018, 26(12): 2549-2561. DOI：

摘要

Abstract

In many batch processes

there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method

and in the feature extraction there is not fully taken into account the characterization of fault information

it will make the process monitoring ineffective

so a fault monitoring method based on WGNPE (weighted global neighborhood preserving embedding)-GSVDD (greedy support vector data description) related and independent variables is proposed. First

mutual information method is used to separate the related variables and independent variables. Secondly

WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information

GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally

the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.

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