Résumé
The design of smart autonomous mechanical structures able to perform online control of their integrity, and take anticipated actions during service before downtime or failure occur, has become an active research area. It is a critical need in various industrial sectors (transport, energy, etc.) for more reliability but also more performance and durability of equipment (aircrafts, wind turbines, bridges, etc.). Implementing such an advanced technology would permit optimized maintenance and capability to operate in degraded mode, managing the decrease of loading capabilities by adapting the operating plan.
However, the real-time monitoring of damage in engineering systems, by dynamically coupling predictive simulation tools (in terms of digital twins) and sensor observations, is made very difficult in practice due to several issues. In particular, the complex nonlinear multiscale phenomena which are involved may be associated with computationally intensive simulations (hardly compatible with real-time), which requires reduced order modeling and effective strategies for data assimilation and control. In addition, the problem is plagued with model bias, uncertain environment, and measurement noise, which need to be taken into account for accurate diagnosis and prognosis, and safe decision-making. In this context, an appealing trend is to refer to hybrid twins, in which an a priori physics-guided model is updated and enriched on-the-fly with data-based information, thus making benefit of all knowledge available.
