Fatigue damage diagnosis and prognosis using bayesian updating

In this paper, a methodology integrating a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction is proposed. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Second, a small time scale model is used as the physics model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result and the small time scale model for RUL prediction. Probability distributions of model parameters are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics.