Structural reliability improvement using non-linear and adaptive multi-model techniques

Structural reliability of a complex structure is related to the residual lifetime of its components. Structural components often contain flaws that propagate due to fatigue and when the crack size becomes critical they eventually fail. In this paper, a number of nonlinear and adaptive identification algorithms are applied to the problem of Fatigue Crack Growth (FCG) monitoring and identification, in order to improve the prediction of the residual time to failure. Several algorithms ranging from simple Non-Linear Least Squares (NLLS) to Extended Kalman Filter (EKF) and adaptive Multi-Model Partitioning algorithms (MMPA) are tested in order to compare their efficiency. As it is shown, using real experimental data, the more advanced identification algorithms have superior performance in estimating future crack size and predicting the residual lifetime of a component.

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