TY - JOUR A1 - Schnur, C. A1 - Goodarzi, P. A1 - Lugovtsova, Yevgeniya A1 - Bulling, Jannis A1 - Prager, Jens A1 - Tschöke, K. A1 - Moll, J. A1 - Schütze, A. A1 - Schneider, T. T1 - Towards interpretable machine learning for automated damage detection based on ultrasonic guided waves N2 - Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. KW - Composite structures KW - Structural health monitoring KW - Carbon fibre-reinforced plastic KW - Interpretable machine learning KW - Automotive industry PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-542060 DO - https://doi.org/10.3390/s22010406 SN - 1424-8220 VL - 22 IS - 1 SP - 1 EP - 19 PB - MDPI CY - Basel AN - OPUS4-54206 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bastuck, M. A1 - Baur, T. A1 - Richter, Matthias A1 - Mull, B. A1 - Schütze, A. A1 - Sauerwald, T. T1 - Comparison of ppb-level gas measurements with a metal-oxide semiconductor gas sensor in two independent laboratories N2 - In this work, we use a gas sensor system consisting of a commercially available gas sensor in temperature cycled operation. It is trained with an extensive gas profile for detection and quantification of hazardous volatile organic compounds (VOC) in the ppb range independent of a varying background of other, less harmful VOCs and inorganic interfering gases like humidity or hydrogen. This training was then validated using a different gas mixture generation apparatus at an independent lab providing analytical methods as reference. While the varying background impedes selective detection of benzene and naphthalene at the low concentrations supplied, both formaldehyde and total VOC can well be quantified, after calibration transfer, by models trained with data from one system and evaluated with data from the other system. The lowest achievable root mean squared errors of prediction were 49 ppb for formaldehyde (in a concentration range of 20–200 ppb) and 150 μg/m³ (in a concentration range of 25–450 μg/m³) for total VOC. The latter uncertainty improves to 13 μg/m³ with a more confined model range of 220–320 μg/m³. The data from the second lab indicate an interfering gas which cannot be detected analytically but strongly influences the sensor signal. This demonstrates the need to take into account all sensor relevant gases, like, e.g., hydrogen and carbon monoxide, in analytical reference measurements. KW - Indoor air quality KW - Volatile organic compounds KW - Calibration transfer KW - Selective quantification KW - Inter-lab comparison PY - 2018 DO - https://doi.org/10.1016/j.snb.2018.06.097 SN - 0925-4005 VL - 273 SP - 1037 EP - 1046 PB - Elsevier B.V. AN - OPUS4-45609 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 -