An extended Kalman filter approach for augmented strain/stress visualization in mechanical systems

This work presents an extended Kalman filtering approach to obtain accurate strain and stress estimates of a structure under operational loading. This information is exploited in an augmented reality application to visualize strains and corresponding stresses on a real component. A parametrized reduced physical model allows an efficient computation of the stresses in the Kalman filter. The model is parametrized in order to give good robustness to uncertain parameters, by estimating the parameters concurrently with the states. In order to allow unknown loading conditions, also the unknown input forces are estimated. This approach offers a very efficient and robust estimation approach. On the other side, using augmented reality as the visualization paradigm, offers two major benefits: visualizing operational strains and stresses field instead of discrete quantities; collocating the results on top of the real component under investigation. The obtained paradigm, validated with a demonstration case through an experimental validation on a beam, permits a more natural visualization and interpretation of operational conditions. Our results encourage the adoption of the proposed approach for on-line monitoring of structural components, opening new possibility in the field of Augmented Reality for Maintenance.