Open AccessResearch on Genetic Algorithm Fault Diagnosis in Chemical Process
Author(s) -
Zhihua Li,
Chen Jing
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/5/052007
Subject(s) - principal component analysis , algorithm , computer science , process (computing) , genetic algorithm , pattern recognition (psychology) , set (abstract data type) , fault (geology) , data set , sample (material) , training set , data mining , artificial intelligence , machine learning , chemistry , chromatography , seismology , programming language , geology , operating system
Aiming at the high false positive rate of traditional PCA (principal component analysis) algorithm in chemical process fault diagnosis, an algorithm combining GA (genetic algorithm) and PCA algorithm is proposed. Optimize the training samples of the PCA algorithm. In this method, when selecting training samples, not only the specific data is selected, but the selected data is optimized by the GA. Select a set of best performing data as a new training sample. Experiments using continuous stirred tank reactor data show that the improved PCA algorithm can effectively reduce the false positive rate compared with the traditional PCA algorithm.