On particle filter improvements for on-line crack growth prognosis with guided wave monitoring

Abstract

Accurate prognosis of fatigue crack growth is of great importance to ensure structural integrity, which is a challenging task due to various uncertainties affecting crack growth. To deal with this problem, the particle filter (PF) based prognostics that incorporates on-line structural health monitoring (SHM) becomes a new trend. However, most existing studies adopt the basic PF algorithm, which needs improvements to meet the requirement for on-line prognosis. It refers to the choice of the importance density and the resampling strategy, as well as the definition of the measurement equation that correlates SHM data to crack states. Till now, no literature addresses this topic in-depth. Aiming at on-line crack growth prognosis, this paper combines four improved PFs with the guided wave based SHM. The study is carried out under two cases respectively, which involve whether or not the measurement equation is accurately trained based on fatigue test data of a kind of aircraft attachment lug. Not only prognostic accuracy and consistency, but also effects of the particle number on the performance and computational cost are analyzed. The result shows advantages and disadvantages of each improved PF for on-line crack growth prognosis, giving instructions to choose appropriate PFs for different application scenarios.