Abstract
The development of a Structural Health Monitoring and Prognosis (SHMP) tool for corrosion degradation and bullet impact damages is matter of SAMAS 2, a project coordinated by the European Defense Agency (EDA) and funded by 2 EDA Member States (Italy and Poland). The aim of SAMAS 2 is to increase the machine availability by directly monitoring the corrosion degradation as well as detecting the ballistic impact occurrence and - in case of crack nucleation - monitoring its propagation inside the structure, concept at the basis of Structural Health Monitoring (SHM) systems. In this way it would be possible to get a real time knowledge about the damage situation, thus setting a Condition Based Maintenance (CBM). The current paper is a summary of the advances in the project, covering all the involved engineering areas, from the adopted sensor network to the FEM simulation based on Reduced Order Modelling techniques and coupling with Machine Learning algorithms (Digital-Twin model), thus coming to the definition of a robust methodology for SHM design. The possibilities for result exploitation, as well as the points requiring further advances, are going to be presented.
Published Version
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