Condition-based prediction of time-dependent reliability in composites. (October 2015)

• Record Type: Journal Article
• Authors: Chiachío, Juan; Chiachío, Manuel; Sankararaman, Shankar; Saxena, Abhinav; Goebel, Kai
• Is Part Of: Reliability engineering & system safety. Volume 142(2015:Oct.)
• Page Start: 134
• Page End: 147
• Publication Date: 2015-10
• Subjects: Model-based prognostics -- Time-dependent reliability -- Fatigue -- Composites; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2015.04.018
• Languages: English
• ISSNs: 0951-8320

Abstract

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. Abstract : 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.