Unsupervised data-driven method for damage localization using guided waves

doi:10.1016/j.ymssp.2023.111038

Return

Unsupervised data-driven method for damage localization using guided waves

2024-02-01

DOI：10.1016/j.ymssp.2023.111038

Original

Original request for help

Save

Abstract

En 中文

Researchers have recently gained interest in ultrasonic guided waves (UGWs) for structural health monitoring (SHM). This interest has mainly grown thanks to the high sensitivity of UGWs to changes in the mechanical and material properties of the medium they travel in. Among the several types of UGWs, Lamb waves (LWs) have been successfully employed to detect, localize and quantify damage in thin-walled structures. To date, localization has been mainly performed through tomographic algorithms. Although those algorithms represent consolidated methods, they come with unsolved issues, such as artifacts generation in damage probability maps and strong dependency on the network of sensors installed to excite and sense diagnostic signals. Hence, more recently, supervised data-driven approaches have been propounded in the literature for LW-based damage diagnosis. Despite the good performance of such methods, a strong limitation is represented by the need for large high-fidelity datasets for training, which are generally not available for real-life structures. A possible solution is represented by unsupervised machine learning methods. Such methods have already been employed for damage detection in the SHM field, but no fully unsupervised approach to damage localization has been proposed yet. Hence, in this work, a framework combining an unsupervised data -driven method and an in-house tomographic approach is presented to process LWs to localize damage. Specifically, convolutional auto-associative neural networks (CAANNs) are employed to process diagnostic signals without requiring any prior feature extraction process. Furthermore, CAANNs predictions are processed to generate damage probability maps. The performance of the proposed method was tested against a numerical case study involving an Aluminum plate and against two experimental datasets of LWs acquired on a full-scale composite wing and a composite plate. Results showed that the proposed method outperformed classic damage diagnosis algorithms in terms of damage localization accuracy.

Keywords:

Unsupervised machine learning

Damage localization

Guided waves

Structural health monitoring

Tomographic algorithm