@inproceedings{7b58a88c8bd046bca8c2e842934b69ef,
title = "A fault diagnosis method in industrial processes with integrated feature space and optimized random forest",
abstract = "Many machine learning methods have been successfully applied in fault diagnosis of industrial processes, while integrated feature space has not been fully considered and utilized. In this paper, a fault diagnosis method with integrated feature space and optimized random forest is proposed to realize accurate diagnosis. By means of the integration of static and dynamic information, an embedded & wrapped feature selection process is designed to provide optimal feature combination for classifier. In addition, the optimal feature combination and hyper-parameter optimization are utilized to construct optimized random forest to improve the generalization of model. Experimental results on Tennessee Eastman benchmark show that the proposed method outperforms traditional approaches with accuracy and F1 score that exceed 87% and 88% respectively.",
keywords = "fault diagnosis, industrial process, integrated feature space, machine learning",
author = "Zhenyu Deng and Te Han and Ruonan Liu and Fengyao Zhi",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 31st IEEE International Symposium on Industrial Electronics, ISIE 2022 ; Conference date: 01-06-2022 Through 03-06-2022",
year = "2022",
doi = "10.1109/ISIE51582.2022.9831753",
language = "English",
series = "IEEE International Symposium on Industrial Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1170--1173",
booktitle = "2022 IEEE 31st International Symposium on Industrial Electronics, ISIE 2022",
address = "United States",
}