Condition-based prediction of time-dependent reliability in composites

Degradation phenomena such as stiffness reduction and increase in matrix micro cracks density are sequentially estimated through a Bayesian filtering framework that incorporates information from both multi-scale damage models and damage measurements, that are sequentially collected along the process.

A set of damage states are further propagated forward in time by simulating the damage progression using the models in the absence of new damage measurements to estimate the time-dependent reliability of the composite material. As a key contribution, the estimation of the remaining useful life is obtained as a probability from the prediction of the time-dependent reliability, whose validity is formally proven using the axioms of Probability Logic.