Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems

Submission Deadline: 4 December 2017

IEEE Access invites manuscript submissions in the area of Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems.

A cyber-physical system (CPS) is a system with intense interaction of entities in the physical world and the abstract information. Such systems commonly exist in both industrial manufacturing and people’s daily lives. As a central investigation topic in Industrial 4.0, the concept of CPS raises vast interests in the academic world as well as in the industrial realm. From the viewpoint of CPS, a bridge is built between the virtual and the real domains, where the strategies of analysis, monitoring, fault diagnosis, control and optimization should be further investigated in a higher systematic level. Typical examples of CPSs include smart grid, autonomous vehicles, process control systems, and industrial robotics systems. The traditional techniques are mainly focused on either the physical object or the abstract data model individually. A simultaneous consideration of both domains is likely to reveal the properties.

Presently, conventional methods of fault diagnosis and control require analytical system models, which are established based on either the physical constraints (first principles) or identification techniques. The feasibility and complexity of these approaches vary significantly among specific applications and the monitoring performance relies heavily on the model precision. On the other hand, data-driven approaches provide a potential solution to large-scale complex systems with better reliability. The motivation of the big data-driven practical solutions originates from the rapid development of digitalized sensors, storage techniques and other information infrastructure, which provide colossal but redundant system information. Consequently, the greatest problem is to design universally feasible solutions to ascertain CPS behavior and to perform fault diagnosis and control design based on the characteristics extracted from the databases and the real-time measurements.

This Special Section in IEEE Access aims to provide a platform for the researchers and participants from both the academic community and industrial sectors to report their recent research and application progress in the field of data-driven CPS monitoring, fault diagnosis and control design.

The topics of interest include, but are not limited to:

  • New data-driven CPS model description and modelling techniques
  • Robust data-driven optimization algorithms for CPSs
  • Unified framework for CPS formulation and analysis
  • Advanced approaches for CPS monitoring
  • Data-Driven CPS fault detection, isolation and diagnosis methods
  • CPS-oriented fault-tolerant control
  • CPS-oriented robust control
  • Data-driven artificial intelligence approaches applied in CPS
  • Challenges and techniques for potential industrial and domestic applications


We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor: Shen Yin, Harbin Institute of Technology, China

Guest Editors:

  1. Okyay Kaynak, Bogazici University , Turkey
  2. Hamid Reza Karimi, Politecnico di Milano, Italy


Relevant IEEE Access Special Sections:

  1. Industry 4.0
  2. Big Data Analytics in Internet of Things and Cyber-Physical Systems
  3. Healthcare Big Data


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: