Abstract：Fatigue growth of cracks in aircraft structures is affected by many aleatory and epistemic uncertainties. In order to effectively track and control such uncertainties by using the prediction results of physical knowledge-based models and observation results of manual inspections, this paper proposes an intelligent digital-twin-based strategy for the inspection and maintenance of aircraft skin cracks. Taking the single edge crack near a rivet hole as the object of study, this strategy takes advantages of the reduced order fracture mechanics simulation, the fatigue crack growth model, the data of crack length inspections, and the framework of the dynamic Bayesian network, comprehensively considering the uncertainties of the initial crack size, material parameters, flight load, measurement error etc., in order to dynamically adjust the inspection and maintenance time according to the probabilistic damage diagnosis results. Simulation results show that the method can effectively track the uncertain crack propagation, and it can provide a referencing basis for the intelligent inspection and maintenance of aircraft skin cracks.
2021, Vol. 42 
