Real- Time sequential monte carlo sampling based on a committee of artificial neural networks for residual lifetime prediction of a component subjected to fatigue crack growth

Abstract

Most of the studies available in the literature about sequential Monte-Carlo sampling algorithm assume that a sufficient number of process observations is available, to guarantee the convergence of the algorithm on the target process evolution. This requirement is remotely met if the process of fatigue crack growth is concerned, due to the costs of maintenance procedures, especially within the aeronautical field. A real- Time diagnostic system is the enabler of the prognostic health monitoring methodology. This work is about the application of sequential Monte-Carlo sampling to estimate the probabilistic residual lifetime of a monitored structural component, subjected to fatigue crack propagation. A real- Time diagnostic unit for crack detection and damage assessment, trained with Finite Element simulations of damage evolution, generates the information as input to the prognostic unit. A crack propagation model provides the knowledge of the residual lifetime prior to the application of fatigue loads. The prognostic unit updates in real- Time the probability density functions of the component residual lifetime at each discrete time during fatigue crack evolution. The methodology is preliminarily applied in simulated environment to an aeronautical metallic panel and the overall performance of a fully autonomous prognostic health monitoring system based upon simulated strain measures is evaluated. © 2014 The Authors. Published by Elsevier Ltd.