Fei He, the associate professor of Collaborative Innovation Center of Steel Technology, received his Ph.D. in mechatronics engineering from University of Science and Technology Beijing in 2010.
Fei He, the associate professor of Collaborative Innovation Center of Steel Technology, received his Ph.D. in mechatronics engineering from University of Science and Technology Beijing in 2010. From 2014 to 2015, he was a visiting researcher in Georgia Institute of Technology. His recent research interest is industry big data, production process quality control and advanced detection technologies, etc.
1. Ministry of Industry and Information Technology, Smart Factory for Wire Steel Rolling, 2018-2019
2. Cooperation Projects, The quality management system for product throughout process via big data analysis of Shagang Steel, 2018-2020
3. Cooperation Projects, The quality management system for Production of Aluminum product process, 2018-2020
4. Fundamental Research Funds of State Key Laboratory, Research on multi-mode statistical modeling and diagnosis of steel strip rolling process, 2019-2020
1. Li Xiaoning, He Fei, Xu Ke. Research on Grain Detection of Grain-Oriented Silicon Steel Based on Two-Dimensional X-Ray Diffraction, Mater. Trans. 2018,59(3): 367-372 （000426039700007）
2. He Fei, Wang Chaojun, Fan Shu-Kai S. Nonlinear fault detection of batch processes based on functional kernel locality preserving projections, Chemometrics and Intelligent Laboratory Systems 183 (2018) 79–89
3. He Fei, Xu Jinwu. A Novel Process Monitoring and Fault Detection Approach based on Statistics Locality Preserving Projections. Journal of process control. 2016，37（1）：46-57
4. He Fei, Yin Anmin, Yang Quan. Grain Size Measurement in Steel by Laser Ultrasonics based on Time Domain Energy. Materials Transactions. 2015，56（6）：808-812
5. He Fei, Li Min, Wang Baojian. Multi-mode acid concentration prediction models of cold-rolled strip steel pickling process. Journal of process control. 2014
6. He Fei, Xu Jinwu, Li Min, Yang Jianhong. Product Quality Modelling and Prediction Based on Wavelet Relevance Vector Machines [J]. Chemometrics and Intelligent Laboratory Systems, 2013, 121:33-41