TY - CONF A1 - Schütze, A. A1 - Tiebe, Carlo T1 - Gas sensor characterization and calibration N2 - This seminar contribution contains the topics: 3S - sensitivity, selectivity and stability; sensor drift, aging and poisoning; influence of ambient conditions; gas mixing systems for sensor characterization and on-site field (re)calibration. T2 - Networked Environmental Monitoring – from sensor principles to novel services CY - BAM, Berlin, Germany DA - 06.02.2018 KW - Gas sensor KW - Gas mixing systems KW - Sensor characterization PY - 2018 AN - OPUS4-44088 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bartholmai, Matthias A1 - Schütze, A. T1 - Sensor network deployment N2 - Content • Fixed monitoring stations • Mobile monitoring stations (on trams, buses, cars) • Personal mobile monitoring systems • Environmental monitoring for agriculture and beyond • Sensors on flying platforms T2 - Networked Environmental Monitoring – from sensor principles to novel services CY - BAM, Berlin, Germany DA - 06.02.2018 KW - Environmental monitoring KW - Gas sensors PY - 2018 AN - OPUS4-44073 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 U6 - 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 - CONF A1 - Tiebe, Carlo A1 - Schütze, A. T1 - Gas sensor characterization and calibration N2 - The lecture about gas sensor characterization and calibration contains the content of 3S: Sensitivity, selectivity and stability, sensor drift, aging and poisoning as well as gas mixing systems for laboratory and field calibration, measurement uncertainty for sensor calibration and traceable calibration of gas sensor systems. T2 - International training course - Low-cost Environmental Monitoring – from sensor principles to novel services CY - Berlin, Germany DA - 09.04.2019 KW - Measurement uncertainty KW - Gas mixing system KW - Sensor drift KW - Sensor aging and poisoning PY - 2019 AN - OPUS4-47850 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Hampel, U. A1 - Schütze, A. A1 - Rädle, M. A1 - Rück, T. A1 - Krawczyk-Becker, M. A1 - Musch, T. A1 - Maiwald, Michael A1 - Fröhlich, H. J. A1 - Zeck, S. T1 - Positionspapier Sensorik für die Digitalisierung chemischer Produktionsanlagen N2 - Die chemische Industrie steht derzeit, wie viele andere Industriebereiche, vor den Herausforderungen einer Digitalisierung der Produktion. Sie ist der Schlüssel für die Flexibilisierung von Prozessen und Anlagen, für die Verkürzung von Produkteinführungszeiten sowie für den Zuschnitt der Produktion auf wechselnde Nachfrage und kürzere Produktlebenszyklen. Die Messtechnik und Sensorik spielt neben der intelligenten Datenverarbeitung eine Schlüsselrolle für die Digitalisierung. Flexiblere Anlagen benötigen Sensorik zur Überwachung des Anlagenzustandes, zur Früherkennung nicht bestimmungsgemäßer Betriebszustände sowie für eine bedarfsgerechte Wartung. Da die Entwicklung neuer und verbesserter Messtechnik und Sensorik grundlegend aus verschiedenen Richtungen gedacht werden muss, haben sich Akteure aus verschiedenen Branchen zusammengetan und dieses Positionspapier erstellt. Es basiert auf einer grundlegenden Analyse des Ist-Stands sowie des Bedarfs der Industrie, die unter anderem auf einem eigens dafür durchgeführten Workshop mit Sensorentwicklern, Anlagenherstellern sowie Anlagenbetreibern am 18. Juni 2019 bei der DECHEMA in Frankfurt a. M. diskutiert wurden. Diese Aktivitäten wurden maßgeblich von der Initiative Wanted Technologies der ProcessNet sowie dem AMA Verband für Sensorik und Messtechnik e.V. initiiert. KW - Prozessindustrie KW - Smarte Sensoren KW - Prozessanalytik KW - DECHEMA KW - Positionspapier PY - 2020 UR - https://dechema.de/Sensorik SP - 1 EP - 20 PB - DECHEMA CY - Frankfurt am Main AN - OPUS4-50403 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schnur, C. A1 - Moll, J. A1 - Lugovtsova, Yevgeniya A1 - Schütze, A. A1 - Schneider, T. T1 - Explainable machine learning for damage detection - In carbon fiber composite plates under varying temperature conditions N2 - 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. T2 - 48th Annual Review of Progress in Quantitative Nondestructive Evaluation CY - Online meeting DA - 28.07.2021 KW - Explainable machine learning KW - Guided waves KW - Damage detection KW - Structural health monitoring KW - Composite structures PY - 2021 SN - 978-0-7918-8552-9 U6 - https://doi.org/10.1115/QNDE2021-75215 SP - 1 EP - 6 PB - American Society of Mechanical Engineers (ASME) CY - New York, NY AN - OPUS4-54219 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schnur, C. A1 - Moll, J. A1 - Lugovtsova, Yevgeniya A1 - Schütze, A. A1 - Schneider, T. ED - Kundu, T. ED - Reis, H. ED - Ihn, J.-B. ED - Dzenis, Y. T1 - Explainable Machine Learning for Damage Detection: in Carbon Fiber Composite Plates Under Varying Temperature Conditions N2 - 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. T2 - 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation CY - Online meeting DA - 28.07.2021 KW - Explainable machine learning KW - Guided waves KW - Damage detection KW - Structural health monitoring KW - Composite structures PY - 2021 SN - 978-0-7918-8552-9 U6 - https://doi.org/10.1115/QNDE2021-75215 VL - QNDE2021-75215 SP - 1 EP - 6 PB - The American Society of Mechanical Engineers (ASME) CY - New York, USA AN - OPUS4-56723 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-542060 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 -