Perspectives on Data-driven Operation Monitoring and Self-optimization of Industrial Processes

• LIU Qiang^{1 , ,}  , 
• ZHUO Jie^{1 ,}  , 
• LANG Zi-Qiang^{2 ,}  , 
• QIN S. Joe^{3 ,} 

• 1.

  State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China

• 2.

  Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK

• 3.

  Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA

Author Bio:

  Master student at Northeastern University, China. Her research interest covers statistical process monitoring, fault diagnosis of complex industrial processes

  Professor at the University of Sheffield, UK. His research interest covers nonlinear system modeling, analysis and design, health monitoring and fault diagnosis of engineering system

  Professor at the University of Southern California, USA. IEEE Fellow, IFAC Fellow and AIChE Fellow. His research interest covers statistical process monitoring, fault diagnosis, model predictive control, system identification, building energy optimization, and control performance monitoring

Corresponding author: LIU Qiang  Associate professor at Northeastern University, China. He was a Research Fellow at University of Southern California during the year of 2014 to 2016. His research interest covers data-driven process modeling, monitoring, and fault diagnosis of complex industrial processes. Corresponding author of this paper



本文责任编委 谢永芳