Feature selection for chemical process fault diagnosis by artificial immune systems

doi:10.1016/j.cjche.2017.09.023

Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (8): 1599-1604.DOI: 10.1016/j.cjche.2017.09.023

• Selected Papers from the Chinese Process Systems Engineering Annual Meeting 2017 • Next Articles

Feature selection for chemical process fault diagnosis by artificial immune systems

Liang Ming, Jinsong Zhao

Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China

Received:2017-09-08 Online:2018-09-21 Published:2018-08-28
Contact: Jinsong Zhao,E-mail address:jinsongzhao@tsinghua.edu.cne
Supported by:
Supported by the National Natural Science Foundation of China (61433001).

Feature selection for chemical process fault diagnosis by artificial immune systems

Liang Ming, Jinsong Zhao

Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China

通讯作者: Jinsong Zhao,E-mail address:jinsongzhao@tsinghua.edu.cne
基金资助:
Supported by the National Natural Science Foundation of China (61433001).

Abstract

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.

Key words: Artificial immune system, Genetic algorithm, Feature selection

摘要： 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.

关键词: Artificial immune system, Genetic algorithm, Feature selection

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]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1599-1604.

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Feature selection for chemical process fault diagnosis by artificial immune systems

Feature selection for chemical process fault diagnosis by artificial immune systems

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