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

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.Structural Integrity & Composite

Utilizing AE data and stochastic modelling towards fatigue damage diagnostics and prognostics of composites

The procedure of damage accumulation in composite materials, especially during fatigue loading, is a complex phenomenon which depends on a number of parameters such as ply orientation, material properties, geometrical non-linearities etc. Towards condition based health monitoring and decision making, the need not only for diagnostic but also for prognostic tools rises and draws increasing attention the last few years. The damage process is in general hidden and manifests itself through in-situ structural health monitoring (SHM) data. Due to the hidden nature of the damage accumulation, non-homogenous hidden Semi Markov process (NHHSMP) seems to be a suitable candidate for describing adequately the aforementioned system’s degradation in time. Its non-homogeneous aspect takes into account the system’s ageing. Moreover, the sojourn times in each state are assumed to be generally distributed, not necessarily exponentially distributed, which is a more realistic assumption for real world engineering systems. The SHM observations are coming from acoustic emission (AE) data recorded throughout constant amplitude fatigue testing of open-hole carbon/epoxy coupons. The scatter of the cycles to failure reported is quite large, an expected result of the stochasticity in the material properties and material inhomogeneities. A maximum likelihood approach for the estimation of the model parameters is followed and useful diagnostic and prognostic measures such as the coupon's current degradation level as well as measures the coupon's remaining useful life (RUL) are proposed for the monitoring of structural integrity of composite materials.Structural Integrity & Composite

In-situ fatigue damage assessment of carbon-fibre reinforced polymer structures using advanced experimental techniques

This study focused on the in-situ fatigue damage assessment of open-hole carbon/epoxy coupons using Acoustic Emission (AE) and Digital Image Correlation (DIC) techniques. Constant amplitude fatigue tests were performed and the main objective was to investigate the damage process, the degradation process of the fatigue modules and to identify features, derived from the experimental data, that can be used as sensitive-to-damage indexes. To this end, the two experimental techniques were reviewed as potential online monitoring tools for the fatigue damage assessment.Structural Integrity & Composite