Special Session II:  “Data-driven fault diagnosis, control, and optimization in modern industrial systems”

Organized by:
Chao Ning, Shanghai Jiao Tong University, China
Jun Shang, Tongji University, China
Hanwen Zhang, University of Science and Technology Beijing, China

Introduction:

Recent years have witnessed an increase in the research of data-driven methods applied in modern industrial systems. Different data-driven process monitoring and fault diagnosis techniques as well as data-driven control and optimization methods have been proposed in the literature, some of which have been successfully applied in considerable industrial processes. Effective control and monitoring methods have significantly increased the efficiency and safety level of modern industrial systems, but there are still a lot of new challenges that need to be solved.
This session is dedicated to exploring innovative data-driven approaches for fault diagnosis, control, and optimization in modern industrial systems. We invite researchers to submit their results; the topics include data-driven process monitoring, fault detection and diagnosis, data-driven control, learning-based model predictive control, data-driven optimization under uncertainty, and other data-driven methods and their applications.

Submission:
Please submit your manuscript via Online Submission System and choose Special Session II: “Data-driven fault diagnosis, control, and optimization in modern industrial systems”.