In-situ fatigue damage analysis and prognostics of composite structures based on health monitoring data

This chapter presents a data-driven probabilistic framework for the in-situ prognostics of composite structures subjected to fatigue loading. The framework deals with the real-time estimation of the remaining useful life based on health monitoring data and a multistate degradation model, the nonhomogeneous hidden semi-Markov model. The motivation of this work lays on the need to predict the remaining useful life accounting for the complex phenomenon of fatigue damage accumulation and the numerous uncertainties that affect it. The methodology was demonstrated during fatigue tests of open-hole carbon epoxy specimens under R = 0. Acoustic emission and strain data were used to extract features sensitive to the fatigue degradation process and a data fusion process was proposed aiming to enhance the prognostics performance. Eight metrics were used to compare the performance between the acoustic emission data, strain and fused data. It was found that based on the selected data fusion process, strain data provided the best predictions.