Condition-based prediction of time-dependent reliability in composites

Juan Chiachio, Manuel Chiachio , Shankar Sankararaman, Abhinav Saxena, Kai Goebel

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)
32 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)134-147
Number of pages14
JournalReliability Engineering and System Safety
Early online date22 May 2015
Publication statusPublished - 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)

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