Dokument-ID Dokumenttyp Autoren/innen Persönliche Herausgeber/innen Haupttitel Abstract Auflage Verlagsort Verlag Herausgeber (Institution) Erscheinungsjahr Titel des übergeordneten Werkes Jahrgang/Band ISBN Veranstaltung Veranstaltungsort Beginndatum der Veranstaltung Enddatum der Veranstaltung Ausgabe/Heft Erste Seite Letzte Seite URN DOI Lizenz Datum der Freischaltung OPUS4-54206 Zeitschriftenartikel Schnur, C.; Goodarzi, P.; Lugovtsova, Yevgeniya; Bulling, Jannis; Prager, Jens; Tschöke, K.; Moll, J.; Schütze, A.; Schneider, T. Towards interpretable machine learning for automated damage detection based on ultrasonic guided waves 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. Basel MDPI 2022 Sensors 22 1 1 19 urn:nbn:de:kobv:b43-542060 10.3390/s22010406 https://creativecommons.org/licenses/by/4.0/deed.de 2022-01-11 OPUS4-56723 Beitrag zu einem Tagungsband Schnur, C.; Moll, J.; Lugovtsova, Yevgeniya; Schütze, A.; Schneider, T. Kundu, T.; Reis, H.; Ihn, J.-B.; Dzenis, Y. Explainable Machine Learning for Damage Detection: in Carbon Fiber Composite Plates Under Varying Temperature Conditions Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics. New York, USA The American Society of Mechanical Engineers (ASME) 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation QNDE2021-75215 978-0-7918-8552-9 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation Online meeting 28.07.2021 30.07.2021 1 6 10.1115/QNDE2021-75215 2022-12-28 OPUS4-54219 Beitrag zu einem Tagungsband Schnur, C.; Moll, J.; Lugovtsova, Yevgeniya; Schütze, A.; Schneider, T. Explainable machine learning for damage detection - In carbon fiber composite plates under varying temperature conditions Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics. New York, NY American Society of Mechanical Engineers (ASME) 2021 Proceedings of the 48th annual review of progress in quantitative nondestructive evaluation 978-0-7918-8552-9 48th Annual Review of Progress in Quantitative Nondestructive Evaluation Online meeting 28.07.2021 30.07.2021 1 6 10.1115/QNDE2021-75215 2022-01-18 OPUS4-45609 Zeitschriftenartikel Bastuck, M.; Baur, T.; Richter, Matthias; Mull, B.; Schütze, A.; Sauerwald, T. Comparison of ppb-level gas measurements with a metal-oxide semiconductor gas sensor in two independent laboratories 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. Elsevier B.V. 2018 Sensors and Actuators B: Chemical 273 1037 1046 10.1016/j.snb.2018.06.097 2018-07-30