A novel approach towards fatigue damage prognostics of composite materials utilizing SHM data and stochastic degradation modeling

Conference paper (2016)

Authors

T. Loutas University of Patras
N. Eleftheroglou Structural Integrity & Composites - Aerospace Engineering
Research Group
Structural Integrity & Composites (Aerospace Engineering) (TU Delft)
Copyright: © 2016 T. Loutas, N. Eleftheroglou
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Published Date

2016

Language

English

Reuse Rights

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Faculty

Aerospace Engineering

Department

Aerospace Structures & Materials

Research Group

Structural Integrity & Composites

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.

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