Feature selection for chemical process fault diagnosis by artificial immune systems  

作　　者：Liang Ming Jinsong Zhao 

机构地区：[1]Department of Chemical Engineering, Tsinghua University [2]Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University

出　　处：《Chinese Journal of Chemical Engineering》2018年第8期1599-1604,共6页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(61433001)

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.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 

分 类 号：TQ019[化学工程]