Dr. Yuan Xu
Professor, PhD Supervisor
Email: xuyuan@mail.buct.edu.cn
Background
Prof. Xu received her PhD degree in Control Science and Engineering from Beijing University of Chemical Technology in 2010 and has published more than 100 papers in the fields of process modeling and optimization, fault detection and diagnosis, alarm design and monitoring, machine learning and system simulation, etc. She is currently serving as a member of Beijing Association for Science and Technology, the executive director and secretary-general of the Beijing Association of Automation, a member of the Process Control Committee of the Chinese Association of Automation, a member of the Industry Artificial Intelligence Committee of the Chinese Association for Artificial Intelligence, a member of the Information Technology Committee of the Chemical Industry and Engineering Society of China, etc. She presided over and participated in many National Key R & D Program of China, the National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation, etc.
Research Interest
Process modeling and optimization
Fault detection and diagnosis
Alarm design and monitoring
machine learning and system simulation
Memberships
Member of Beijing Association for Science and Technology
Executive director and secretary-general of the Beijing Association of Automation
Member of the Process Control Committee of the Chinese Association of Automation
Member of the Industry Artificial Intelligence Committee of the Chinese Association for Artificial Intelligence
Member of the Information Technology Committee of the Chemical Industry and Engineering Society of China
Teaching Assignment
Undergraduate Teaching
Artificial Intelligence Principles and Applications
Fundamentals of Artificial Intelligence
Postgraduate Teaching
Safety Systems Engineering
Projects
National Key R & D Program of China
Elastic computing and intelligent analysis technology for end-to-end cloud collaboration
Cyber- physical theory and system architecture for high confidence city
National Natural Science Foundation of China
Researches on disaster risk evolution prediction for hazardous chemicals of petrochemical industry under the conditions of imbalanced and small sample datasets
Research on key technologies of alarm identification analysis and dependence root-cause for complex industry process
Research on extension fault diagnosis method for chemical processes
Beijing Municipal Natural Science Foundation
Research on key techniques of accident prediction and virtual display for dangerous chemicals based on extension theory and VRGIS
Some Representative Publications
Yuan Xu, Xue Jiang, Wei Ke, Qunxiong Zhu, Yanlin He. A novel pattern classification integrated global-local preserving projections with improved adaptive rank-order morphological filter for fault diagnosis, Process Safety and Environmental Protection, 2023, 171: 299-311
Xue Jiang, Yuan Xu*, Wei Ke, Yang Zhang, Qunxiong Zhu, Yanlin He. An imbalanced multi-fault diagnosis method based on bias weights adaBoost, IEEE Transactions on Instrumentation and Measurement, 2022, 71, 1-8.
Yuan Xu, Kaiduo Cong, Yang Zhang, Qunxiong Zhu, Yanlin He. A novel Adaboost ensemble model based on the reconstruction of local tangent space alignment and its application to multiple faults recognition, Journal of Process Control, 2021, 104: 158-167
Yuan Xu, Cuihuan Fan, Qunxiong Zhu, Abbas Rajabifard, Nengcheng Chen, Yiqun Chen, Yanlin He. Novel pattern-matching integrated KCVA with adaptive rank-order morphological filter and its application to fault diagnosis, Industrial & Engineering Chemistry Research, 2020, 59(4): 1619-1630
Yuan Xu, Shengqi Shen, Yanlin He, Qunxiong Zhu. A novel hybrid method integrating CA-PCA with relevant vector machine for multivariate process monitoring, IEEE Transactions on Control Systems Technology, 2019, 27(4): 1780-1784
Yuan Xu, Mingqing Zhang, Liangliang Ye, Qunxiong Zhu, Zhiqiang Geng, Yanlin He, Yongming Han. A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction, Energy, 2018, 164: 137-146