Dynamic Network Toolbox for Data-Driven Model Learning and Diagnostics

Increasing demands on the safe and efficient operation of engineering systems require the ability to model, monitor, optimize and control complex dynamic systems that are spatially interconnected as networks of dynamic subsystems. Examples can be found e.g., in distributed (smart) power systems, industrial production processes, transportation networks, biomedical systems, etcetera.Operational decisions are being made on the basis of models and data, while keeping the network models up-to-date over time is key for the ability to guarantee safe, efficient and robust operation. While sensor data is playing an increasing role as a basis for modeling, diagnostics, decision making and predictive maintenance, there are currently no standard software tools available for data analytics and machine learning where effective use is made of the physical interconnection structure of the constituting subsystems. The results of the ERC Advanced Research project Data-driven modeling in dynamic networks (2016-2022) will be translated into an effective general purpose GUI supported MATLAB-based toolbox for data analytics, including data-driven dynamic modelling and diagnostics, for the situation of spatially interconnected linear dynamic systems. The scientific methods and algorithms will be turned into into effective workflows to be used by engineers, researchers and students for applications in a variety of engineering domains. A professional software architecture and first steps of an implementation have already been realized. The project will be guided by stakeholders from industry, among which MathWorks Inc, ASML and ABB, who will also be involved in the development of use cases. Parallel to this project, the research into effective methods will continue, allowing to overcome shortcomings in the current tools that might appear. A further plan for exploitation of the toolbox will be made by the end of the project, and will be dependent on the proof-of-concept evaluation.