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.
|Number of pages||14|
|Journal||Reliability Engineering and System Safety|
|Early online date||22 May 2015|
|Publication status||Published - 31 Oct 2015|
- fatigue degradation
- composite materials
- condition-based prediction
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Best Paper Award
Chiachio-Ruano, Juan (Recipient), Jul 2014
Prize: Prize (including medals and awards)