Condition-based prediction of time-dependent reliability in composites

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

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

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.
LanguageEnglish
Pages134-147
Number of pages14
JournalReliability Engineering and System Safety
Volume142
Early online date22 May 2015
DOIs
Publication statusPublished - 31 Oct 2015

Fingerprint

Composite materials
Degradation
Fatigue damage
Laminates
Stiffness
Fatigue of materials
Cracks
Carbon

Keywords

  • fatigue degradation
  • composite materials
  • condition-based prediction

Cite this

Chiachio, Juan ; Chiachio , Manuel ; Sankararaman, Shankar ; Saxena, Abhinav ; Goebel, Kai. / Condition-based prediction of time-dependent reliability in composites. In: Reliability Engineering and System Safety. 2015 ; Vol. 142. pp. 134-147.
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Condition-based prediction of time-dependent reliability in composites. / Chiachio, Juan; Chiachio , Manuel; Sankararaman, Shankar; Saxena, Abhinav; Goebel, Kai.

In: Reliability Engineering and System Safety, Vol. 142, 31.10.2015, p. 134-147.

Research output: Contribution to journalArticle

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