TY - CONF A1 - Memmolo, V. A1 - Lugovtsova, Yevgeniya A1 - Olino, M. A1 - Prager, Jens ED - Kundu, T. ED - Reis, H. ED - Ihn, J.-B. T1 - Application of Temperature Compensation Strategies for Ultrasonic Guided Waves to Distributed Sensor Networks N2 - Temperature compensation strategies play a key role in the implementation of guided wave based structural health monitoring approaches. The varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively assesses two temperature compensation methods, namely the optimal baseline selection (OBS) and the baseline signal stretch (BSS), with the aim to extend their use to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS and BSS are investigated considering multiple couples of sensors employed in the DSN. A decision strategy that uses frequent value warning to define the optimal baseline or stretching parameter is found to be effective analyzing data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combinations of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS and BSS approach making use of fewer signal processing algorithms. In addition, the effectiveness of those approach is quantified using damage indicators as metric, which confirms that the performance of OBS and BSS quantitatively agree with predictions and also demonstrate that the use of compensation strategies improve detectability of damage with a higher reliability of the system. T2 - 2022 49th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2022) CY - San Diego, CA, USA DA - 25.07.2022 KW - Ultrasound KW - Elastic waves KW - Structural Health Monitoring KW - Environmental Effects KW - Damage Detection PY - 2022 SN - 978-0-7918-8659-5 DO - https://doi.org/10.1115/QNDE2022-98534 SP - 1 PB - The American Society of Mechanical Engineers (ASME) CY - 2 Park Avenue, New York, NY 10016, USA AN - OPUS4-57074 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - El Moutaouakil, H. A1 - Fuchs, C. A1 - Savli, E. A1 - Heimann, Jan A1 - Prager, Jens A1 - Moll, J. A1 - Tschöke, K. A1 - Márquez Reyes, O. A1 - Schackmann, O. A1 - Memmolo, V. A1 - Schneider, T. T1 - Acquiring a Machine Learning Data Set for Structural Health Monitoring of Hydrogen Pressure Vessels at Operating Conditions using Guided Ultrasonic Waves N2 - Hydrogen is an energy source of increasing importance. As hydrogen is very reactive to air and needs to be stored under high pressure, it is crucial to provide safe transportation and storage. Therefore, structural health monitoring, based on guided ultrasonic waves and machine learning methods, is used for Composite Overwrapped Pressure Vessels (COPVs) containing hydrogen. To acquire data that allows robust detection of COPV defects, there are two main process parameters to consider. These are the pressurization of the vessel and the temperature conditions at the vessel. This paper will focus on the derivation of a design of experiment (DoE) from the needs of various validation scenarios (e.g. concerning pressure, temperature or excitation frequency). Practical limitations must be considered as well. We designed experiments with multiple reversible damages at different positions. A network of 25 transducers, structured as five rings with five sensors in one line, is installed on a vessel. Guided ultrasonic waves are used via the pitch-catch procedure, which means that the transducers act pairwise as transmitter and receiver in order to measure all transmitterreceiver combinations. This leads to 600 signal paths, recorded by a Verasonics Vantage 64 LF data acquisition system. Finally, the influences of temperature and pressure within the acquired data set are going to be visualized. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Composite Overwrapped Pressure Vessel KW - Hydrogen KW - Guided Ultrasonic Waves KW - Data Acquisition KW - Pressurization KW - Machine Learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607472 DO - https://doi.org/10.58286/29754 SN - 1435-4934 SP - 1 EP - 8 AN - OPUS4-60747 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 -