TY - JOUR A1 - El Moutaouakil, H. A1 - Heimann, Jan A1 - Lozano, Daniel A1 - Memmolo, V. A1 - Schütze, A. T1 - Feature Extractor for Damage Localization on Composite-Overwrapped Pressure Vessel Based on Signal Similarity Using Ultrasonic Guided Waves N2 - Hydrogen is one of the future green energy sources that could resolve issues related to fossil fuels. The widespread use of hydrogen can be enabled by composite-overwrapped pressure vessels for storage. It offers advantages due to its low weight and improved mechanical performance. However, the safe storage of hydrogen requires continuous monitoring. Combining ultrasonic guided waves with interpretable machine learning provides a powerful tool for structural health monitoring. In this study, we developed a feature extraction approach based on a similarity method that enables interpretability in the proposed machine learning model for damage detection and localization in pressure vessels. Furthermore, a systematic optimization was performed to explore and tune the model’s parameters. This resulting model provides accurate damage localization and is capable of detecting and localizing damage on hydrogen pressure vessels with an average localization error of 2 cm and a classification accuracy of 96.5% when using quantized classification. In contrast, binarized classification yields a higher accuracy of 99.5%, but with a larger localization error of 6 cm. KW - Ultrasonic Guided Waves KW - Composite Overwrapped Pressure Vessel KW - Interpretable Machine Learning KW - Structural Health Monitoring KW - Damage Localization KW - Critical Infrastructure KW - Hydrogen KW - Non-destructive Testing PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640249 DO - https://doi.org/10.3390/app15179288 VL - 15 IS - 17 SP - 1 EP - 20 PB - MDPI AN - OPUS4-64024 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -