Improving rotating machinery with a digital twin
Collecting data from a fleet of installed products can improve condition monitoring and predictive maintenance services.
Embedded sensors and actuators combined with modern networking, cloud, and machine learning technologies made it possible to collect and analyze massive amounts of data reflecting the use of industrial products. This data explosion provides obvious opportunities to optimize the operation of products and systems in terms of energy consumption, material usage, or quality control. Collecting data from a fleet of installed products can improve condition monitoring and predictive maintenance services as well as further value adding services.
In the research project the behavior of rotating machinery will be improved using a digital twin coupled with Industrial Internet methods to support enhanced data flow between the machinery, simulation based virtual sensors, and applied big data analytics. This will lead to insights into how the rotating machinery design can be improved, in addition to better operational efficiency of the machinery and enhanced quality of the products manufactured with them. The wider scientific objective is to study how Industrial Internet methodologies coupled with machine learning can be applied especially to complex engineering design.
The project Digital Twin of Rotor System is funded by the Academy of Finland and lasts until the end of 2019. The project is conducted together with Lappeenranta University of Technology.
Aalto Industrial Internet Campus
Professor Petri Kuosmanen