Sequential Monte Carlo sampling for crack growth prediction providing for several uncertainties

Abstract

The problem of fatigue crack growth monitoring and residual lifetime prediction is faced by means of sequential Monte Carlo methods commonly defined as sequential importance sampling/resampling or particle filtering techniques. The algorithm purpose is the estimation of the fatigue crack evolution in metallic structures, considering uncertainties coming from phenomenological aspects and material properties affecting the process. These multiple uncertainties become a series of unknown parameters within the framework of the dynamic state-space model describing the crack propagation. These parameters, if correctly estimated within the particle filtering algorithm, will cover the uncertainties coming from the real environment, improving the prognostic performances. The standard particle filter formulation needs additional methods to augment the state vector and to correctly estimate the parameters. The prognostic system composed by the sequential Monte Carlo algorithm able to account for different uncertainties is tested through several crack growth simulations. The applicability of the method to real structures and the employment in presence of real environmental conditions (i.e. variable loading conditions) is also discussed at the end of the paper.