Impact localisation on composite aircraft structures remains a significant challenge due to operational and environmental uncertainties, such as variations in temperature, impact mass and energy levels. This study proposes a novel Gaussian process regression (GPR) framework that leverages the order invariance of time difference of arrival (TDOA) inputs to achieve probabilistic impact localisation under such uncertainties. A composite (COMP) kernel function, combining radial basis function and cosine similarity kernels, is designed based on wave propagation dynamics to enhance adaptability to diverse conditions. To jointly predict spatial coordinates, a task covariance kernel is incorporated to support multitask learning, allowing the model to capture correlations between outputs. To further improve robustness, a Bayesian-inspired model averaging strategy is employed to fuse predictions from multiple GPR models, assigning adaptive weights based on both global model fit and local predictive confidence. The proposed framework is experimentally validated on a sensorised composite panel under a wide range of impact conditions, including large-mass drop tower tests and small-mass guided impacts, across varying temperatures and angles. Convolutional neural networks, a widely used deep learning method, are adopted as a baseline for comparison. Results demonstrate that the GPR-based approach achieves higher localisation accuracy and robustness without requiring explicit compensation for environmental or loading variations. The study also highlights the critical role of TDOA preprocessing: sample standardisation outperforms feature standardisation by preserving directional structure and improving GPR model compatibility. These findings underscore the method’s potential for reliable, uncertainty-aware structural health monitoring in complex aerospace environments.
Xiao et al. (Thu,) studied this question.
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