A Bayesian approach for damage assessment in welded structures using Lamb-wave surrogate models and minimal sensing

Abstract

The increasing mechanical and economical demands in modern systems and structures are forcing an inevitable need for joining dissimilar materials, thus creating the challenge of establishing a process to inspect and monitor dissimilar joints. Condition monitoring is a necessity to ensure that the structures are being safely used and to extend their lifetime. Lamb waves (LWs) are ultrasonic guided waves that are widely used for structural health monitoring of mechanical, aerospace, and civil structures. This paper proposes a novel structural health monitoring approach for damage detection, localization, and assessment using a minimal LW sensor-actuator set-up. More specifically, the proposed framework provides damage detection, localization and assessment within a dissimilar-material joint by Bayesian inference of six parameters of damage extent and location. Finite element simulations are used to simulate the measured LWs and generate a dataset required to train artificial neural networks (ANN), acting as surrogate models for LW simulation with reduced computational cost. The ANN-based LW simulations are further used as forward model within an Approximate Bayesian Computation (ABC) framework to provide probabilistic inference of the damage size and position. The results show that damage of different sizes and locations can be successfully identified with a high level of resolution and with quantified uncertainty. The results also show that data fusion for ABC inference using multiple sensor measurements can be possible with improved inference results. However, a precise and robust damage inference can be achieved using a minimal sensing set-up based on one actuator and two sensing points, with consideration of certain levels of measurement noise. These findings imply a considerable reduction of complexity of LW actuator-sensor networks, and overall, they imply a significant reduction of computational resources and cost for damage detection and assessment in structures, thus providing a step forward towards online/onboard monitoring applications.