Physics-Informed Neural Network for baseline-free damage diagnosis using Ultrasonic Guided Waves
A. Casartelli, L. Lomazzi, Marco Giglio, F. Cadini
Ultrasonic Guided Waves (UGWs) are among the most effective tools for damage diagnosis and Structural Health Monitoring (SHM) of thin-walled structures. However, traditional SHM methods based on UGWs typically require baseline measurements and signal post-processing to extract damage indices, which can lead to the loss of important information contained in the raw data. To face these challenges, recent studies have investigated machine learning approaches. However, most of these methods depend on large labeled datasets and use black-box models with limited interpretability. In this work, a Physics-Informed Neural Network (PINN) framework is proposed to overcome these limitations. The PINN solves an inverse problem by simultaneously reconstructing the full ultrasonic wavefield and the spatial distribution of the wave velocity, expressed in terms of Young’s modulus, recognizing potential discontinuities caused by damage. This is achieved by minimizing a combined loss function that enforces both the agreement with sparse measurement data and the physical laws governing the propagation of UGWs. The proposed method is numerically validated on a case study of an isotropic structure affected by damage. The results demonstrate that the approach can accurately localize damage without the need for data post-processing. Moreover, the method is fully unsupervised and baseline-free, relying solely on sparse measurements and the underlying physical laws of UGW propagation.