Prognosis of fatigue cracks in an aircraft wing using an adaptive tunable network and guided wave based structural health monitoring

Abstract

Due to the stochastic behaviour of fatigue crack propagation resulting from the multifarious uncertainties such as aleatory uncertainty and epistemic uncertainty, a synthetic scheme integrating heterogeneous uncertainty resources is needed to make the prognosis of fatigue crack propagation be accurate. In this paper, an on-line updating strategy combining an adaptively tunable hybrid radial basis function (RBF) network and active Lamb waves based structural health monitoring is proposed for prognosis of fatigue cracks in an aircraft wing. A particle filter and Brownian motion with drift and proportionality coefficients are put forward to dynamically access a posteriori estimation of crack state and model parameters. Because of the advantages of flexibility, electrically stabilized and suitable for complex structure, the sensor layer with an embedded network of distributed piezoelectric transducers is utilized to collect Lamb wave signals describing crack states on-line. A hybrid spatial phase difference-based damage index (DI) is employed to capture the characteristics of Lamb wave signals to quantitatively determine the crack length. Furthermore, fatigue tests on the outboard wing of a real airplane are conducted to substantiate the proposed method. The experimental results demonstrate that the hybrid RBF model can timely trap the dynamic characteristics of sensor signals, and effectively predict the varying tendency of cracks growth. The uncertainties during the prognosis of fatigue cracking propagation can be effectively dealt with by combining the dynamic adjustability and the DIs-based on-line monitoring technique.