Fatigue damage diagnostics and prognostics of composites utilizing structural health monitoring data and stochastic processes

Abstract

The procedure of damage accumulation in composites, especially during fatigue loading, is a complex phenomenon of stochastic nature which depends on a number of parameters such as type and frequency of loading, stacking sequence, material properties, and so on. Toward condition-based health monitoring and decision making, the need for not only diagnostic but also prognostic tools rises and draws increasing attention in the last few years. To this direction, we model the damage evolution in composites as a doubly stochastic hidden Markov process that manifests itself via structural health monitoring observations, that is, acoustic emission data. The damage process is modeled via an extension of the classic hidden Markov models to account for nonhomogeneity, that is, age dependence in state transitions. The observations come from acoustic emission data recorded throughout fatigue testing of open-hole carbon–epoxy coupons. A procedure that utilizes multiple observation sequences from a training dataset and estimates in a maximum likelihood sense the optimal model parameters is presented and applied in unseen data via a cross-validation rationale. Diagnostics of the most likely health state determination, average degradation level, and prognostics of the remaining useful life are among the capabilities of the presented stochastic model.