Condition-based prediction of time-dependent reliability in composites

This paper presents a reliability-based prediction methodology to obtain the remaining useful life of composite materials subjected to fatigue degradation. 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. A case study is presented using multi-scale fatigue damage data from a cross-ply carbon-epoxy laminate. - Highlights: • A prognostics framework is proposed to predict fatigue damage in composite materials. • The prognostics methodology is formulated to incorporate uncertainties. • Time-dependent reliability predictions are obtained using particle filters. • RUL is straightforwardly obtained from the calculation of time-dependent reliability. • A case study is presented using multi-scale fatigue damage data