A novel approach towards fatigue damage prognostics of composite materials utilizing SHM data and stochastic degradation modeling
T. Loutas (University of Patras)
N. Eleftheroglou (TU Delft - Structural Integrity & Composites)
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Abstract
A prognostic framework is proposed in order to estimate the remaining useful life of composite materials under fatigue loading based on acoustic emission data and a sophisticated Non Homogenous Hidden Semi Markov Model. Bayesian neural networks are also utilized as an alternative machine learning technique for the non-linear regression task. A comparison between the two algorithms operation, input, output and performance highlights their ability to tackle the prognostic task.