Setting Adaptive Inspection Intervals in Helicopter Components, Based on a Digital Twin

Abstract

Setting inspection intervals based on an accurate prediction of fatigue crack sizes is essential for sustaining the integrity of aeronautical structures. However, the fatigue crack growth and its prognosis are affected by various uncertainties, which makes the current inspection strategy with fixed intervals challenging in managing the aircraft with diverse damage states in a fleet. In this study, an intelligent crack inspection strategy is proposed based on a digital twin, in which a reduced-order fracture mechanics simulation methodology, a validated fatigue crack growth model, and the historical crack length inspection results are integrated into a dynamic Bayesian network. The proposed strategy uses two connected probabilistic processes, which conduct the diagnosis/prognosis and calculate the inspection intervals, respectively, to adaptively set the inspection intervals according to the updating of the digital twin model. The proposed inspection strategy is demonstrated by the various crack growth histories of a helicopter component and benchmarked against several baselines. The results show that the probability of failure can be kept below the threshold, even though the initial crack size and the crack growth parameters are underestimated in the prior distribution. Further applications on more realistic aircraft structures will be carried out in the future.