Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Wende Tian; Zijian Liu; Lening Li; Shifa Zhang; Chuankun Li

doi:10.1016/j.cjche.2020.05.003

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Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

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

- Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning
- Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning
- 中国化学工程学报（英文版） 2020年28卷第7期页码：1875-1883
- Affiliations：
  
  1. College of Chemical Engineering, Qingdao University of Science&Technology,Qingdao,China,266042
  2. Qingdao NOVAN Electromechanical Technology,Qingdao,China,266100
  3. State Key Laboratory of Safety and Control for Chemicals, SINOPEC Qingdao Research Institute of Safety Engineering,Qingdao,China,266071
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2020.05.003
  中图分类号：
- 纸质出版：2020
- Accepted：
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Wende Tian, Zijian Liu, Lening Li, 等. Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning[J]. 中国化学工程学报（英文版）, 2020,28(7):1875-1883.

Wende Tian, Zijian Liu, Lening Li, Shifa Zhang, Chuankun Li. Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning[J]. Chinese Journal of Chemical Engineering, 2020, 28(7): 1875-1883.
Wende Tian, Zijian Liu, Lening Li, 等. Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning[J]. 中国化学工程学报（英文版）, 2020,28(7):1875-1883. DOI： 10.1016/j.cjche.2020.05.003.

Wende Tian, Zijian Liu, Lening Li, Shifa Zhang, Chuankun Li. Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning[J]. Chinese Journal of Chemical Engineering, 2020, 28(7): 1875-1883. DOI： 10.1016/j.cjche.2020.05.003.

摘要

Abstract

Identification of abnormal conditions is essential in the chemical process. With the rapid development of artificial intelligence technology

deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently. In the high-dimensional data identification using deep neural networks

problems such as insufficient data and missing data

measurement noise

redundant variables

and high coupling of data are often encountered. To tackle these problems

a feature based deep belief networks (DBN) method is proposed in this paper. First

a generative adversarial network (GAN) is used to reconstruct the random and non-random missing data of chemical process. Second

the feature variables are selected by Spearman's rank correlation coefficient (SRCC) from high-dimensional data to eliminate the noise and redundant variables and

as a consequence

compress data dimension of chemical process. Finally

the feature filtered data is deeply abstracted

learned and tuned by DBN for multi-case fault identification. The application in the Tennessee Eastman (TE) process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process

compared with the traditional fault identification algorithms.

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