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Eingeladener Vortrag
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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.
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.
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.
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.
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.
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.
Sensor network deployment
(2018)