Fault diagnosis of electric porcelain firing kiln process based on random forest and XGBoost
Author:
Affiliation:
1. University of Jinan,School of Information Science and Engineering,Jinan,China
2. University of Jinan,School of Electrical Engineering,Jinan,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10023546/10022325/10023623.pdf?arnumber=10023623
Reference8 articles.
1. Nonparametric feature selection by random forests and deep neural networks[J];xiaojun;Computational Statistics and Data Analysis,2022
2. Fault detection and diagnosis for the screw chillers using multi-region XGBoost model[J];shuai;Sci Technol Built Environ,2021
3. Mathematical model to investigate the influence of circulation systems on the firing of ceramics
4. XGBoost
5. Greedy function approximation: A gradient boosting machine.
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