Workshop on Data-driven Fault Diagnosis with Collaborative Computing

In recent years, fault diagnosis has developed into a major research area at the intersection of system and control engineering, applied mathematics and statistics with the development in state of the art applications including internet of things and cyber physical systems. Not only are intelligent components and devices implemented and networked, but real-time supervision and control systems are also running in parallel which brings the convenience for collaborative computing. This workshop aims to provide a forum for researchers and industrial practitioners to exchange their latest results on data-driven fault diagnosis techniques with collaborative computing, and the papers to be accepted in this workshop are expected to provide the latest developments in data driven fault diagnosis approaches, especially new theoretical results with practical applications.

The topics of interest include, but are not limited to:
1. Data-driven diagnosis methods;
2. Condition monitoring and fault prediction;
3. Data-driven process supervision;
4. Data-driven performance evaluation and decisions;
5. Data-driven optimization methods;
6. Real-time model-free learning methods;
7. Soft computing methods for fault diagnosis;
8. Active fault diagnosis methods;
9. Application topics in cyber-physical systems, networked systems, electrical and mechatronic systems, power systems, and robotics.

Paper submission
Papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the Springer format (see Author’s kit section).

Workshop chair
Dr. Rui Yang
Department of Computer Science and Software Engineering
Xi’an Jiaotong-Liverpool University
Suzhou, China