Probabilistic method for damage identification in multi-layered composite structures

The barely visible damages sustained in multi-layered composites can severely jeopardise the structural integrity and operational safety in real-life applications. Traditional intrusive inspections in condition monitoring can significantly contribute to the cost and time overhead of such operation and is susceptible to errors when relying on manual inspection workflow. Acoustic emission (AE) techniques have received increasing attention in recent years for complex composite structures under service loads. AE is based on detecting acoustic energy emitted from damages sustained in structures (such as fatigue fracture, fibre breakage, amongst others). The AE monitoring technique requires solving an inverse problem where the measured signals are linked to the source and nature of damage developed in the structure. However, given the significant uncertainty around all real-life measurements of structures under operating loads, such as sporadic signals from multiple sources, reflection from boundaries or irregular geometric interfaces and measurement noise, it is essential to explicitly account for these uncertainties in the damage identification algorithms. The current work framework of automated probabilistic damage detection which explicitly models the parameterized uncertainties and conditions them based on measurement data to give probabilistic descriptors of damage metrics. The empirical relationship modelling the AE as a function of damage properties is calibrated with a training dataset. During the online monitoring phase, the spatially correlated time data is utilized in conjunction with the calibrated AE empirical model to infer the probabilistic description of the acoustic emission source within a hierarchical Bayesian inference framework. The methodology is tested on a composite structure consisting of carbon fibre panel with stiffeners subjected to impact and dynamic fatigue loading. The study presents a generalized automated AE-based damage detection methodology which is applicable for structures with different geometrical and material properties under conditions of external loading.