An efficient algorithm to predict the expected end-of-life in composites under fatigue conditions

This work presents an efficient computational framework for estimating the end of life (EOL) and remaining useful life (RUL) by combining the prognostics principles with the technique of Subset simulation. It has been named PFP-SubSim on behalf of the full denomination of the computational framework, namely, particle filter-based prognostics using Subset Simulation. It is shown that the resulting algorithm is especially useful when dealing with the prognostics of evolving processes with asymptotic behaviors where the length of the dataset is limited, as observed in practice for many fatigue degradation processes in composites. Its efficiency is demonstrated on data collected from run-to-failure tension-tension fatigue experiments measuring the evolution of fatigue damage in CRFP cross-ply laminates using PZT sensors for obtaining data of matrix micro-crack density.