Fault monitoring based on mutual information feature engineering modeling in chemical process

doi:10.1016/j.cjche.2018.11.008

摘要/Abstract

摘要： A large amount of information is frequently encountered when characterizing the sample model in chemical process. A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper. From the whole process point of view, the method makes use of the characteristic of mutual information to select the optimal variable subset. It extracts the correlation among variables in the whitening process without limiting to only linear correlations. Further, PCA (Principal Component Analysis) dimension reduction is used to extract feature subset before fault diagnosis. The application results of the TE (Tennessee Eastman) simulation process show that the dynamic modeling process of MIFE (Mutual Information Feature Engineering) can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

关键词: Big data, Fault diagnosis, Mutual information, TE process, Process modeling

Abstract: A large amount of information is frequently encountered when characterizing the sample model in chemical process. A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper. From the whole process point of view, the method makes use of the characteristic of mutual information to select the optimal variable subset. It extracts the correlation among variables in the whitening process without limiting to only linear correlations. Further, PCA (Principal Component Analysis) dimension reduction is used to extract feature subset before fault diagnosis. The application results of the TE (Tennessee Eastman) simulation process show that the dynamic modeling process of MIFE (Mutual Information Feature Engineering) can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

Key words: Big data, Fault diagnosis, Mutual information, TE process, Process modeling

Wende Tian, Yujia Ren, Yuxi Dong, Shaoguang Wang, Lingzhen Bu. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. 中国化学工程学报, 2019, 27(10): 2491-2497.

Wende Tian, Yujia Ren, Yuxi Dong, Shaoguang Wang, Lingzhen Bu. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. Chinese Journal of Chemical Engineering, 2019, 27(10): 2491-2497.

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