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A digital twin test bench was created to demonstrate the digital twin concept for control and prediction of the dynamic behavior of a paper machine roll. The paper presents a complete proof of concept digital twin system with wireless sensoring with flexible measurement patterns, data transfer, storage and visualization in an interactive 3D view. A virtual sensor based on a recurrent neural network was created to predict the middle cross section center point movement of a large flexible rotor based on acceleration and force input measured from bearing housings at the ends of the rotor. The results show that a neural network algorithm is feasible for predicting the dynamic behaviour of the rotor system. Future research at Aalto University aims to apply sensor fusion data as input to non-physics based models with the goal to predict key performance indicators of complex mechanical systems.

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