TY - CONF A1 - Schackmann, Oliver A1 - Márquez Reyes, Octavio A1 - Memmolo, Vittorio A1 - Lozano, Daniel A1 - Prager, Jens A1 - Moll, Jochen A1 - Kraemer, Peter T1 - Intelligent damage detection in composite pressure vessels under varying environmental and operational conditions N2 - Despite proven approaches available in the literature, structural health monitoring by ultrasonic guided waves under varying environmental and operational conditions is still challenging. The use of machine learning approaches is discussed in this work, considering the complex problem of experimental damage detection under varying load conditions in a composite overwrapped pressure vessel for hydrogen storage. Specifically, unsupervised methods originally developed for image and time series classification are combined with ensemble voting to conceive reliable damage detection technique. This enables the effective combination of the predictions of multiple transducer pairs, even with a limited number of strong individual classifiers. A performance demonstration of the technique is presented using a real damage scenario dataset. T2 - IEEE 12th International Workshop on Metrology for AeroSpace (MetroAeroSpace) CY - Napoli, Italy DA - 18.06.2025 KW - Guided ultrasonic waves KW - Structural health monitoring KW - Artificial intelligence KW - Hydrogen storage PY - 2025 SN - 979-8-3315-0152-5 DO - https://doi.org/10.1109/MetroAeroSpace64938.2025.11114628 SN - 2575-7490 SP - 608 EP - 613 AN - OPUS4-64894 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -