Modern demands on the safe and efficient operation of engineering systems require the ability to model, monitor, optimize and control dynamic systems that are spatially interconnected as networks of dynamic systems. Examples can be found e.g. in distributed (smart) power systems, industrial production and manufacturing processes, transportation networks, etcetera. While the global behavior of the systems is the target of optimization, a single centralized (global) control and monitoring infrastructure is not viable anymore for realizing safe and efficient operation that can handle local disturbances and changes in the local systems and their dynamic properties, and/or changes in the interconnection structure.
In order for the systems operations to be able to adapt to changing circumstances and to diagnose system changes/faults, the use of sensor data in combination with dynamic models is of paramount importance. While sensor data is playing a tremendously increasing role as a basis for diagnostics, decision making and predictive maintenance, there are currently no standard software tools available for data analytics, data-driven modelling and machine learning, where effective use is made of the physical interconnection structure of the constituting subsystems.
The problem to be addressed is to provide engineers, designers, researchers and engineering students with an effective general purpose software toolbox for data analytics, including data-driven dynamic modelling and diagnostics, for the situation of spatially interconnected systems, based on recently developed methods and tools in dynamic network identification.
When successful, this software toolbox, implemented in the commonly used MATLAB environment, can serve as an important design and engineering tool for large scale dynamic systems operations, and used by researchers, engineers and designers in engineering offices, industrial research and development departments and universities.
