Fault monitoring based on mutual information feature engineering modeling in chemical process

Wende Tian; Yujia Ren; Yuxi Dong; Shaoguang Wang; Lingzhen Bu

doi:10.1016/j.cjche.2018.11.008

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Fault monitoring based on mutual information feature engineering modeling in chemical process

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

- Fault monitoring based on mutual information feature engineering modeling in chemical process
- Fault monitoring based on mutual information feature engineering modeling in chemical process
- 中国化学工程学报（英文版） 2019年27卷第10期页码：2491-2497
- Affiliations：
  
  College of Chemical Engineering, Qingdao University of Science & Technology,Qingdao,China,266042
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2018.11.008
  中图分类号：
- 纸质出版：2019
- Accepted：
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Wende Tian, Yujia Ren, Yuxi Dong, 等. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. 中国化学工程学报（英文版）, 2019,27(10):2491-2497.

Wende Tian, Yujia Ren, Yuxi Dong, Shaoguang Wang, Lingzhen Bu. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. Chinese Journal of Chemical Engineering, 2019, 27(10): 2491-2497.
Wende Tian, Yujia Ren, Yuxi Dong, 等. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. 中国化学工程学报（英文版）, 2019,27(10):2491-2497. DOI： 10.1016/j.cjche.2018.11.008.

Wende Tian, Yujia Ren, Yuxi Dong, Shaoguang Wang, Lingzhen Bu. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. Chinese Journal of Chemical Engineering, 2019, 27(10): 2491-2497. DOI： 10.1016/j.cjche.2018.11.008.

摘要

Abstract

A large amount of information is frequently encountered when characterizing the sample model in chemical process. A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper. From the whole process point of view

the method makes use of the characteristic of mutual information to select the optimal variable subset. It extracts the correlation among variables in the whitening process without limiting to only linear correlations. Further

PCA (Principal Component Analysis) dimension reduction is used to extract feature subset before fault diagnosis. The application results of the TE (Tennessee Eastman) simulation process show that the dynamic modeling process of MIFE (Mutual Information Feature Engineering) can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

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