Research on Genetic Algorithm Fault Diagnosis in Chemical Process

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Li, Zhihua^1 ; Chen, Jing^1

School of Automation, Wuhan University of Technology, Wuhan

430070, China^1

关键词: Chemical process;    Continuous stirred tank reactor;    False positive rates;    GA (genetic algorithm);    PCA (principal component analysis);    PCA algorithms;    Training sample;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/5/052007/pdf
DOI  :  10.1088/1757-899X/563/5/052007

来源: IOP

【 摘 要 】

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.