Digital twins

Abstract: The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins.

Abstract: This paper proposes a digital twin formulation for on-orbit servicing operations involving multiple spacecraft operating in resource-constrained and uncertain scenarios. Our problem setup considers multiple spacecraft, each comprised of multiple subsystems, and develops a modular digital twin formulation that incorporates state estimation, information sharing, and compatible control strategies across the various subsystem digital twins, with a goal of driving the system-of-systems towards mission success. The proposed formulation is implemented and demonstrated for a simulated on-orbit servicing mission featuring a controllable, health-aware chaser spacecraft performing a rendezvous with an uncontrollable target spacecraft. The modular digital twin formulation handles uncertainty in the kinematic states as well as the health of the propulsion subsystem, while producing optimal control strategies that are robust to the considered modes of failure.