A Procedure for Performing Reproducibility Assessment of the Accuracy of Impact Area Classification for Structural Health Monitoring in Aerospace Structures

The principal objective of this work is to develop an optimized procedure that guarantees the reproducibility of results across different applications and laboratories, facilitating potential field applications of methodologies for Structural Health Monitoring in aerospace structures. The focus is to accurately detect and localize impact areas on planar structures using in situ transducers and Machine Learning (ML) techniques. The research concentrates on an aluminum plate where impacts are generated by metal spheres of different masses dropped from a fixed height. The resulting Lamb waves are detected by PZT sensors glued on the surface. Various data processing and feature extraction algorithms are implemented and compared to extract the differences in Time of Flight (ΔToF). The obtained features are used for training ML classification models. Then, the influence of various parameters in signal acquisition and data processing are assessed along with the reproducibility of the results. For this reason, an interlaboratory comparison is conducted in which the trained models are applied to data collected under varying conditions. The experimental results show that the most influencing factors for impact area classification are the algorithm for ΔToF estimation, the number of training points used in ML models, the type of classification model, the distribution of the impact points on the component, and their balance in the classification area. This evidence suggests approaches for reducing both issues, therefore improving the reproducibility of results.