Damage Prognosis Framework for Monitored Structural Systems with Multi-Site Fatigue-Driven Damage Growth

摘要

Fatigue-induced damage is one of the roost uncertain and extremely unpredictable failure mechanisms for a large variety of structural systems subjected to random cyclic loading during service life. Among these systems, composite lightweight aerospace structures-such as fighter aircrafts and unmanned aerial vehicles (UAVs)-are particularly sensitive to both fatigue- and impact-induced damage. Therefore, a system capable of (i) monitoring the critical components of these structural systems, (ii) assessing their structural integrity, and (iii) predicting their remaining life (damage prognosis) is ultimately needed. This study provides an overview of a probabilistic methodology for predicting the remaining fatigue life of adhesively-bonded joints in composite structures. According to this methodology, nondestructive evaluation (NDE) techniques and recursive Bayesian inference are used to (i) assess the current state of damage and, (ii) update the probability distributions of both the damage extent and the damage evolution model parameters at various damage locations after each NDE inspection. The propagation of damage is then stochastically simulated using a probabilistic model for future operational loads and a surrogate model (calibrated using a mechanics-based model) for the structural response of interest. Finally, local and global failure criteria are considered simultaneously to compute the probabilities of failure and false-alarm at future times by abstracting the real structure (or structural component) into a combination of series and parallel sub-systems. The application example provided in this study analyzes the fatigue-driven debonding propagation along a pre-defined adhesive interface in a simply supported laminated composite beam, demonstrates the efficiency of the proposed recursive Bayesian inference scheme, and shows the use of the proposed component and system reliability analyses to recursively predict and update the evolution in time of the probabilities of failure and false-alarm of the structure.