Feature selection for chemical process fault diagnosis by artificial immune systems

Liang Ming; Jinsong Zhao

doi:10.1016/j.cjche.2017.09.023

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Feature selection for chemical process fault diagnosis by artificial immune systems

Selected Papers from the Chinese Process Systems Engineering Annual Meeting 2017 | Updated：2026-01-06

- Feature selection for chemical process fault diagnosis by artificial immune systems
- Feature selection for chemical process fault diagnosis by artificial immune systems
- 中国化学工程学报（英文版） 2018年26卷第8期页码：1599-1604
- Affiliations：
  
  1. Department of Chemical Engineering, Tsinghua University,Beijing,China,100084
  2. Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University,Beijing,China,100084
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.09.023
  中图分类号：
- 纸质出版：2018
- Accepted：
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Liang Ming, Jinsong Zhao. Feature selection for chemical process fault diagnosis by artificial immune systems[J]. 中国化学工程学报（英文版）, 2018,26(8):1599-1604.

Liang Ming, Jinsong Zhao. Feature selection for chemical process fault diagnosis by artificial immune systems[J]. Chin.J.Chem.Eng., 2018, 26(8): 1599-1604.
Liang Ming, Jinsong Zhao. Feature selection for chemical process fault diagnosis by artificial immune systems[J]. 中国化学工程学报（英文版）, 2018,26(8):1599-1604. DOI： 10.1016/j.cjche.2017.09.023.

Liang Ming, Jinsong Zhao. Feature selection for chemical process fault diagnosis by artificial immune systems[J]. Chin.J.Chem.Eng., 2018, 26(8): 1599-1604. DOI： 10.1016/j.cjche.2017.09.023.

摘要

Abstract

With the Industry 4.0 era coming

modern chemical plants will be gradually transformed into smart factories

which sets higher requirements for fault detection and diagnosis (FDD) to enhance operation safety intelligence. In a typical chemical process

there are hundreds of process variables. Feature selection is a key to the efficiency and effectiveness of FDD. Even though artificial immune system has advantages in adaptation and independency on a large number of fault samples

antibody library construction used to be based on experience. It is not only time consuming

but also lack of scientific foundation in fault feature selection

which may deteriorate the FDD performance of the AIS. In this paper

a fault antibody feature selection optimization (FAFSO) algorithm is proposed based on genetic algorithm to optimize the fault antibody features and the antibody libraries' thresholds simultaneously. The performance of the proposed FAFSO algorithms is illustrated through the Tennessee Eastman benchmark problem.

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