Damage State Estimation via Multi-fidelity Gaussian Process Regression Models for Active-Sensing Structure Health Monitoring

Guided wave-based techniques have been used extensively in Structural Health Monitoring (SHM). Models using guided waves can provide information from both time and frequency domains to make themselves accurate and robust. Probabilistic SHM models, which have the ability to account for uncertainties, are developed when decision confidence intervals are of interest. However, most existing active-sensing guided-wave methods are based on the requirement that a relatively large data set can be collected, and thus are not feasible when data collection is restricted by time or environmental conditions. Meanwhile, simulation results, though lack of accuracy compared to real world data, are easier to obtain. In this context, models that incorporate data from different sources have the potential to embrace the accuracy of experimental data and the convenience of simulated data without the necessity of large and, potentially costly experimental, data sets. The goal of this work is to introduce and assess a probabilistic multi-fidelity Gaussian process regression framework for damage state estimation via the use of both experimental and simulated guided waves. The main differences from previous works include the combination of damage-sensitive features (damage indices; DIs) extracted from experimental and numerical sources, and a relatively small amount of real-world data. The proposed model is validated using data from two sources. The experimental data is collected from a piezoelectric sensor network attached to an aluminum plate under varying crack sizes while the simulated data comes from multi-physics finite element model (FEM) simulations with the same specifications.