TY - CONF A1 - Stamm, Michael T1 - In situ Charakterisierung von Reparaturen in GFK Schalenstrukturen mittels Lockin Thermografie und Felddehnungsmessungen N2 - Die tiefgreifende Reparatur von Faserverbundwerkstoffen stellt durch deren inneren Aufbau eine besondere Herausforderung dar, die unter anderem bei der Instandhaltung von Rotorblättern von Windkraftanlagen zum Tragen kommt. Um die Reparaturen von Rotorblattschalen zu untersuchen und zu optimieren, wurden an der BAM Sandwich-Schalenprüfkörper aus Glasfaserkunststoff (GFK) entwickelt. Nachdem in diese Schalenprüfkörper Modell-Reparaturen eingebracht wurden, werden zyklische mechanische Ermüdungsversuche bis zum Bruch durchgeführt. Vorrangiges Ziel ist es, den Einfluss verschiedener Reparaturvariablen auf die Ermüdungslebensdauer der Struktur zu verstehen, sowie die stabilste Reparaturmethode zu bestimmen. Während der zyklischen Lastversuche wurden regelmäßig in-situ Messungen mit einer Thermografie Kamera und einem optischen Felddehnungsmesssystem durchgeführt. Durch beide Messmethoden lassen sich Eigenschaften der Probekörper zeitlich und räumlich aufgelöst darstellen. Während die Felddehnungsmessungen Aufschluss über die Verformung an der Oberfläche geben, erfasst die thermische Inspektion Temperaturänderungen an der Oberfläche. Besonders die thermografischen Messwerte bilden hier das oberflächennahe Volumen ab, in dem sich Temperaturänderungen an der Oberfläche abzeichnen. Die Messergebnisse beider Systeme stellen also verschiede aber korrelierte Parameter dar. Denn die zyklische Belastung (Bewegung) ruft eine periodische Temperaturänderung im Bauteil hervor. Dieser so genannte Thermoeleastische Effekt kann durch eine Lockin-Analyse der Temperaturtransienten (Temperaturverläufe) nachgewiesen werden. Die räumliche Darstellung der thermischen Effekte lässt Rückschlüsse auf die tatsächliche Lastverteilung im Probekörpervolumen zu. Darüber hinaus führen nichtelastische Verformungen zu einer zusätzlichen Wärmefreisetzung, die durch geeignete Auswerteverfahren ebenfalls nachgewiesen werden können. Entsprechende Hot-Spots in der räumlichen Verteilung erlauben eine frühzeitige Erkennung besonders belasteter Bereiche lange vor dem eigentlichen Bauteil-Versagen. Das gestattet die Lokalisierung des Initial-Schadens, was mit der numerischen Spannungsanalyse korreliert werden kann und die Entwicklung und Bewertung verschiedener Reparaturstrategien. T2 - DGZfP Jahrestagung 2022 CY - Kassel, Germany DA - 23.05.2022 KW - Lockin Thermografie KW - GFK KW - NDT KW - ZfP PY - 2022 AN - OPUS4-55823 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lay, Vera T1 - Das Themenfeldprojekt SealWasteSafe im Überblick N2 - Das Projekt SealWasteSafe verbessert Werkstofftechnik, Prüfverfahren und Überwachungsmethoden für sichere Verschlussbauwerke in Endlagern. Dieser Überblick fasst die laufenden Arbeiten in folgenden Bereichen zusammen: 1) Materialentwicklung und Dauerhaftigkeit; 2) Überwachung; 3) Inspektion. T2 - Online-Workshop „Dauerhafte Verschlussbauwerke für Endlager“ CY - Online meeting DA - 14.02.2022 KW - SealWasteSafe KW - Salzbeton KW - Verschlussbauwerke KW - Monitoring KW - Inspektion KW - Ultraschall PY - 2022 AN - OPUS4-55089 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Effner, Ute T1 - Ultraschall-Messungen an in-situ-Abdichtbauwerken mit neu entwickelten Messtechniken N2 - Neue Ultraschall-Messsysteme (LAUS, Bohrlochsonde) haben ihre Funktionstüchtigkeit unter Beweis gestellt. Ergebnisse aus den Messungen konnten verifiziert werden. Standard-Mess- und –Auswerteverfahren müssen für Spritzbeton weiterentwickelt und angepasst werden. Mit sämtlichen Messmethoden konnten großflächige Risse detektiert werden. Messungen mit der Bohrlochsonde und LAUS sind zur Qualitätssicherung geeignet. T2 - Workshop zum TFP-SealWasteSafe CY - Online meeting DA - 14.02.2022 KW - Abdichtbauwerk / Verschlussbauwerk KW - Zerstörungsfreie Prüfung KW - Ultraschall-Echoverfahren PY - 2022 AN - OPUS4-55104 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin T1 - Distributed fiber optic radiation sensors for nuclear applications N2 - Advantages and underlying principles of distributed fiber optic radiation sensing are discussed. Differences with regard to dose measurement range and resolution between glass optical fiber based sensing and polymer optical fiber based sensing are outlined. T2 - DGZfP SMIRT26 Laborbesuch (BAM) CY - Berlin, Germany DA - 10.07.2022 KW - Distributed fiber optic sensing KW - Fiber optic radiation monitoring KW - Distributed fiber optic radiation sensing PY - 2022 AN - OPUS4-55264 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lay, Vera T1 - Geophysikalische Methoden für die Zerstörungsfreie Prüfung im Bauwesen N2 - Im Rahmen des Seminars "Angewandte Forschungsthemen (SAFT)" am Institut für Geowissenschaften der Christian-Albrechts-Universität zu Kiel werden verschiedene geophysikalische Methoden vorgestellt, die bei der zerstörungsfreien Prüfung im Bauwesen zum Einsatz kommen. Neben einem Überblick der Arbeiten an der BAM im Allgemeinen und dem Fachbereich im Besonderen geht es vor allem um den Einsatz von Ultraschallmethoden. Drei wesentliche Schwerpunkte werden dabei beleuchtet: 1) (Geophysikalische) Antworten auf Herausforderungen bei Ultraschall-Anwendungen 2) Material research and multi-sensory monitoring for concrete sealing structures in rock salt unterground repositories (Projekt SealWasteSafe) und 3) Untersuchung von Spritzbeton-Probekörpern unter Tage mit Ultraschall zur Qualitätssicherung von Verschlussbauwerken. Insgesamt werden diverse Möglichkeiten für die Geophysik für Forschung und Studierende im Bereich der zerstörungsfreien Prüfung im Bauwesen aufgezeigt. T2 - Seminar Angewandte Forschungsthemen CY - Kiel, Germany DA - 14.06.2023 KW - SealWasteSafe KW - Ultraschall KW - Verschlussbauwerke KW - Abbildungsverfahren PY - 2023 AN - OPUS4-57828 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Friedrich, Christian T1 - Eignungsprüfungen geophysikalischer Sensoren zur Rissdetektion an Demonstrationsbauwerken N2 - Bei der Betonage von Abdichtbauwerken können Temperaturen bis 110 °C und hohe Drücke bis 5 MPa erreicht werden. Mittels Technikumsversuchen wurde nachgewiesen, dass Mikroakustik- und Ultraschallsensoren zum Einbau in Demonstrationsbauwerke geeignet sind. Die Untersuchungen wurden bei der BAM (Bundesanstalt für Materialforschung und -prüfung) in Berlin durchgeführt. Es ist vorgesehen, die Sensoren in Morsleben am Demonstrationsbauwerk im Anhydrit einzusetzen. T2 - 84. Jahrestagung der Deutschen Geophysikalischen Gesellschaft CY - Jena, Germany DA - 10.03.2024 KW - Ultraschall KW - Eignungsprüfung KW - Risse KW - Abdichtbauwerk KW - Anhydrit PY - 2024 AN - OPUS4-59772 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - EvalTherm - Weather-dependent passive thermography and thermal simulation of in-service wind turbine blades N2 - To cope with the increase in the manufacturing and operation of wind turbines, wind farm operators need inspection tools that are able to provide reliable information while keeping the downtime low. Current inspection techniques require to stop the wind turbine. This work presents the current progress in the project EvalTherm, in which passive thermography is evaluated as a possible non-destructive inspection tool for operational wind turbine blades (WTBs). A methodology to obtain thermal images of rotating WTBs has been established in this project. However, the quality of the results is heavily dependent on various aspects such as weather conditions, information on the inspected WTB, damage history, etc. In this work, a section of a used WTB is simulated using finite-element modelling (FEM) as well as experimentally tested for evaluating the accuracy of the model. Such a model will provide insight into the potential thermal response of a certain structure (with specific material properties) in given weather (boundary) conditions. The model is able to provide satisfactory predictions of the thermal response of the structure, as well as indicate what thermal contrast(s) result from artificial defects introduced in the structure. T2 - WindEurope Annual Event 2023 CY - Copenhagen, Denmark DA - 25.04.2023 KW - FEM KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie anlage rotorblätter PY - 2023 AN - OPUS4-58274 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Sablowski, J. A1 - Lugovtsova, Yevgeniya A1 - Bulling, Jannis A1 - Kupsch, C. T1 - Experimental Study of Ultrasonic Guided Waves in a Bipolar Plate of a Fuel Cell N2 - Water management in proton-exchange membrane fuel cells (PEMFCs) is critical for efficient operation. However, in-situ measurements of the water distribution in an operating PEMFC are challenging. In this study, we investigate key aspects of ultrasonic guided wave (UGW) propagation through the bipolar plate of a PEMFC using 3D scanning laser Doppler vibrometry (3D LDV). Our results provide insights for the design and optimization of UGW-based sensor systems for water detection in PEMFCs. T2 - 2023 IEEE International Ultrasonics Symposium (IUS) CY - Montreal, QC, Canada DA - 03.09.2023 KW - Ultrasonic guided waves KW - Laser Doppler vibrometry KW - Fuel cells KW - Water management PY - 2023 DO - https://doi.org/10.1109/IUS51837.2023.10307327 SP - 1 EP - 4 PB - IEEE AN - OPUS4-59540 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ghasem Zadeh Khorasani, Media T1 - Federated SHM: a Novel Collaborative ML Approach to Overcome Data Scarcity and Data Privacy Concerns in StructuralHealth Monitoring N2 - The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities. Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations. In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence. Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems. T2 - QI Digital Forum CY - Berlin, Germany DA - 10.10.2023 KW - Machine learning KW - Federated learning KW - Structural health monitoring KW - Artificial intelligence KW - Hydrogen safety KW - Data privacy PY - 2023 AN - OPUS4-59415 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Charmi, Amir A1 - Mustapha, Samir A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Prager, Jens T1 - A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels N2 - The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy. T2 - 13th European Conference on Non-Destructive Testing (ECNDT) 2023 CY - Lisbon, Portugal DA - 03.07.2023 KW - Machine learning KW - Structural health monitoring KW - COPV KW - Guided waves KW - Damage localization PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-590789 DO - https://doi.org/10.58286/28079 SN - 2941-4989 VL - 1 IS - 1 SP - 1 EP - 6 PB - NDT.net CY - Mayen AN - OPUS4-59078 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mustapha, S. A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Brence, Blaz A1 - Prager, Jens T1 - Guided Waves Propagation in Composite Overwrapped Pressure Vessel N2 - The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification. T2 - 13th. European Conference on Non-Destructive Testing (ECNDT) CY - Lisbon, Portugal DA - 03.07.2023 KW - Composite Overwrapped Pressure Vessels KW - Guided Waves KW - Laser Doppler Vibrometer KW - Structural Health Monitoring KW - Sensors Placement PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-591081 DO - https://doi.org/10.58286/28068 SN - 2941-4989 VL - 1 IS - 1 SP - 1 EP - 6 PB - NDT.net CY - Mayen AN - OPUS4-59108 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Mustapha, S. A1 - Charmi, Amir A1 - Prager, Jens T1 - Guided wave ultrasonic feature determination in Type IV composite overwrapped pressure vessels towards the digital twin N2 - The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations. T2 - 13th European Conference on Non-Destructive Testing (ECNDT) CY - Lisbon, Portugal DA - 03.07.2023 KW - Industry 4.0 KW - Composite overwrapped pressure vessels KW - Structural health monitoring KW - Ultrasonic guided waves KW - Predictive maintenance PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-591094 DO - https://doi.org/10.58286/28072 SN - 2941-4989 VL - 1 IS - 1 SP - 1 EP - 6 PB - NDT.net CY - Mayen AN - OPUS4-59109 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heimann, Jan T1 - Guided Waves Propagation in Composite Overwrapped Pressure Vessel N2 - The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0,2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification. T2 - European Conference on Non-Destructive Testing (ECNDT) CY - Lisbon, Portugal DA - 03.07.2023 KW - Structural Health Monitoring KW - Guided Waves KW - Composite Overwrapped Pressure Vessel KW - Laser Doppler Vibrometer KW - Sensor Placement PY - 2023 AN - OPUS4-58873 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Charmi, Amir T1 - Closing the gap between simulation and experiment using autoencoders N2 - Structural Health Monitoring (SHM) mittels geführter Ultraschallwellen ermöglicht eine kontinuierliche Überwachung komplexer Bauteile und liefert umfangreiche Informationen über deren strukturelle Integrität. Die aufgezeichneten Signale sind jedoch komplex und werden daher zunehmend mittels KI verarbeitet, um Anomalien in der Systemintegrität automatisch zu erkennen. Eine Herausforderung dabei ist jedoch das Trainieren solcher KI-Modelle, da dafür große Mengen an Trainingsdaten inklusive diverser Schadensfälle benötigt werden, um eine hohe Genauigkeit und Zuverlässigkeit zu gewährleisten. Experimentelle Generierung dieser Daten ist wegen des enormen Zeit- und Kostenaufwands nicht möglich. Eine Alternative hierfür bieten die numerischen Simulationen, da dadurch Trainingsdaten für beliebige Anzahl an Schadenskonfigurationen erzeugt werden können. Das Hauptproblem dabei ist jedoch die Abweichung zwischen den experimentellen und simulierten Daten (durch z.B. Abweichungen bei der Geometrie, Randbedingungen oder Materialparameter). Um diese Abweichungen zu reduzieren bzw. zu eliminieren, wird ein KI-Modell mit einer Autoencoder-Decoder Architektur vorgestellt, die mittels eines Experiments kalibriert werden kann und anschließend in der Lage ist, die Genauigkeit der Simulationsergebnisse nachträglich zu erhöhen. T2 - 28. DEGA-Workshop „Physikalische Akustik“ - Maschinelles Lernen in der physikalischen Akustik CY - Bad Honnef, Germany DA - 19.10.2023 KW - KI KW - SHM KW - Simulation KW - Geführte Ultraschallwellen PY - 2023 AN - OPUS4-58686 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Charmi, Amir T1 - A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels N2 - The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy. T2 - European Conference on Non-Destructive Testing (ECNDT) CY - Lisbon, Portugal DA - 03.07.2023 KW - Guided waves KW - Structural Health Monitoring KW - Machine learning KW - Damage localization KW - COPV PY - 2023 AN - OPUS4-58687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Melzer, Michael A1 - Nattuveettil, Keerthana T1 - Digital Calibration Certificates: Transforming Efficiency and Safety in Hydrogen Refuelling Station N2 - Digital Certificates have emerged as a pivotal element in automation and digitalisation. This presentation highlights the added value of a digitalised metrology, its impact on the workflows on the calibration service providing and receiving side as well as their significance in enhancing the quality infrastructure. An overview of digital calibration certificates (DCC) for temperature sensors, including their structure and role in establishing trust in hydrogen refuelling stations (HRS), will be discussed. Additionally, it explores the impact of DCC on optimising efficiency in the hydrogen refuelling process. Exploring the paradigm shift brought about by Industry 4.0, where machines possess the ability to autonomously interpret digital certificate data, leads to streamlined safety checks and reduced human intervention. We will discuss how automated verification of machine-readable certificates contributes to maintaining and elevating safety standards over human-readable certificates. Furthermore, we will take a deep dive into the application of DCCs in HRS, showcasing how they enhance operational efficiency, accuracy, and maintenance by enabling real-time monitoring and adjustment of process data. By exploring the interdependent relationship between digital certificates, machine-readable environments, and HRS optimisation, this presentation will provide valuable insights into harnessing cutting-edge technologies to create a safer, more efficient, and technologically empowered hydrogen refuelling process. T2 - H2Safety@BAM : Hydrogen Colloquium CY - Online meeting DA - 06.09.2023 KW - Digital Calibration Certificate KW - Digitalization KW - Digital Traceability KW - QI-Digital KW - Hydrogen Infrastructure PY - 2023 UR - https://www.bam.de/Content/DE/Standardartikel/Themen/Energie/Wasserstoff/wasserstoff.html AN - OPUS4-58353 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - Weather-dependent passive thermography and thermal simulation of in-service wind turbine blades N2 - The thermographic methods NDT division of BAM is aiming to develop a wind turbine blade (WTB) inspection tool using infrared thermographic scanning of operational wind turbines in the project EvalTherm. T2 - WindEurope Annual Event 2023 CY - Copenhagen, Denmark DA - 25.04.2023 KW - FEM KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie anlage rotorblätte PY - 2023 AN - OPUS4-58275 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chaudhuri, Somsubhro A1 - Stamm, Michael A1 - Krankenhagen, Rainer T1 - Weather-dependent passive thermography and thermal simulation of in-service wind turbine blades N2 - . To cope with the increase in the manufacturing and operation of wind turbines, wind farm operators need inspection tools that are able to provide reliable information while keeping the downtime low. Current inspection techniques require to stop the wind turbine. This work presents the current progress in the project EvalTherm, in which passive thermography is evaluated as a possible non-destructive inspection tool for operational wind turbine blades (WTBs). A methodology to obtain thermal images of rotating WTBs has been established in this project. However, the quality of the results is heavily dependent on various aspects such as weather conditions, information on the inspected WTB, damage history, etc. In this work, a section of a used WTB is simulated using finite-element modelling (FEM) as well as experimentally tested for evaluating the accuracy of the model. Such a model will provide insight into the potential thermal response of a certain structure (with specific material properties) in given weather (boundary) conditions. The model is able to provide satisfactory predictions of the thermal response of the structure, as well as indicate what thermal contrast(s) result from artificial defects introduced in the structure. T2 - WindEurope Annual Event 2023 CY - Copenhagen, Denmark DA - 25.04.2023 KW - FEM KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie Anlage Rotorblätter PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-582777 DO - https://doi.org/10.1088/1742-6596/2507/1/012025 VL - 2507 SP - 1 EP - 12 PB - IOP Publishing Ltd. AN - OPUS4-58277 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - EvalTherm - Detectability of internal defects in wind turbine rotor blades using passive infrared thermography N2 - A steady increase of wind energy infrastructure brings along a challenge of maintaining and operating wind turbines (WT) with its multiple components. Inspection of wind turbine rotor blades (WTB) is an important part of maintaining the overall health and safety of a WT. It involves visually or mechanically examining the blades for signs of damage or wear that could affect their performance and structural integrity of the entire WT. A WTB is a complex structure due to its ever-increasing scale (going beyond 100 m for a 16 MW WT) as well as multi-material construction. Passive infrared thermography offers an alternative to contact- or proximity-based inspection techniques currently in use such as visual inspection performed by technical personnel (using a lift or a drone) and involves looking for signs of damage on the surface of the blades, and ultrasonic testing to detect internal defects. In contrast to active thermography, passive thermography uses the sun as source of heat, instead of conventional heat lamps, flash, or laser. An inspection technique to (semi-autonomously) inspect the WTBs of an operating WT from the ground has been developed. Given the optimum thermal contrast (weather conditions for field measurements), external as well as internal features of the WTB can be visualised with appropriate post-processing. The work presented here is part of an ongoing multi-partner project titled “EvalTherm”: the evaluation of passive thermography as a non-destructive inspection tool of WTBs in operation. In this work, artificial defects representative of realistic defects in glass fibre reinforced plastic (GFRP) WTBs are introduced in out-of-service WTB pieces. These are scanned using X-ray computed tomography to obtain a three-dimensional reconstruction to be used as input for finite-element based thermal simulation using COMSOL Multiphysics. The simulation data is compared with infrared thermal inspection of the same WTB section, in order to compare the effect of thermal contrast caused in certain weather conditions. In addition, the influence of defect characteristics such as defect size, morphology, and location on detectability is investigated. Validated simulation models are used to predict thermal signatures of defects along with the optimal thermal contrast. Such simulation models in combination with weather forecast data can assist operators of wind turbine infrastructure to plan passive thermography inspection without the need of dangerous inspection procedures and associated shutdown of energy production. T2 - Wind Energy Science Conference (WESC) 2023 CY - Glasgow, Scotland DA - 23.05.2023 KW - FEM KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie anlage rotorblätter KW - Thermography PY - 2023 AN - OPUS4-58283 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mehri Sofiani, F A1 - Chaudhuri, Somsubhro A1 - Elahi, S.A. A1 - Hectors, K. A1 - De Waele, W. T1 - Quantitative Analysis of the Correlation between Geometric Parameters of Pits and Stress Concentration Factors for a Plate Subject to Uniaxial Tensile Stress N2 - The offshore environment is inherently corrosive. Consequently, pits may nucleate on exposed steel surfaces. Corrosion pits can be a source of crack initiation when the structure is subject to fatigue loading. The criticality of a corrosion pit with respect to the structural integrity depends on its shape and size and can be quantified using a stress concentration factor (𝐾𝑡). In this work, a parametric 3D finite element model is developed to perform stress analysis of a pitted plate subjected to uniaxial tensile stress. The model is used for an extensive parameter study in which 𝐾𝑡 is determined for various pit configurations. It is demonstrated that each one of the geometrical parameters holds a substantial influence on the location of the Most Critical Region (MCR). It is shown that 𝐾𝑡 increases as the pit gets narrower. Pits with an elliptical mouth yield higher 𝐾𝑡 values when the angle between the load direction and the pit mouth major axis increases. Moreover, 𝐾𝑡 increases with the increase in the localized thickness loss which is more pronounced for relatively wider pits. Finally, a regression model is presented for estimating 𝐾𝑡 based on the geometric parameters of a pit. KW - FEM KW - Corrosion KW - SCF KW - Pitting corrosion PY - 2023 DO - https://doi.org/10.1016/j.tafmec.2023.104081 SN - 0167-8442 VL - 127 SP - 1 EP - 27 PB - Elsevier Ltd. AN - OPUS4-58284 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Semi-automated detection of rain erosion damages on turbine blades with passive thermography and AI image processing N2 - The European Green Deal and the global fight against climate change call for more and larger wind turbines in Europe and around the world. To meet the increasing demand for maintenance and inspection, partly autonomous methods of remote inspection are increasingly being developed in addition to industrial climbers performing the inspection. Rotor blades are exposed to extreme weather conditions throughout their lifetime of 20 years, and the leading edge erodes over time. These erosion damages change the aerodynamic features of blades and can cause structural damages. The estimated annual energy production (AEP) losses caused by erosion damages are between 0.5% and 2% per year. The classification of the severity of a rain erosion damage and the quantification of the resulting AEP losses for cost efficient repair and maintenance efforts and improved power production of wind turbines are subject of scientific research. For the inspection of wind turbine rotor blades, passive thermography is an option that has been used to detect both internal damage [3, 4] as well as erosion on the surface [5, 6]. The advantage is that, given suitable boundary conditions, not only the rain erosion damage itself but also temperature differences caused by the resulting turbulences can be observed on the surface of the blade. Turbulences reduce the efficiency of the rotor blades and result in AEP losses. Optimised thermography inspections can contribute to detect and to evaluate rain erosion damages. The thermal inspection lasts 10 minutes per turbine and is performed while the turbine is in full operation, avoiding downtime and lost opportunities for the turbine owner which are usually caused by conventional blade inspections. The timely inspection procedure is complemented by an automatic data evaluation and results in a considerable number of inspected wind turbines in a certain period of time. A fully convolutional network (FCN) is implemented for the automated evaluation of thermal images. In the presented study, more than 1000 thermographic images of blades were annotated, augmented and used to train and test the FCN. The aim is the precise marking of thermal signatures caused by erosion damage at the leading edge. The area size of the detected temperature difference caused by turbulences was used to identify and categorise damages. Certain strategies were adopted to group small individual indications as one large damage, in order to develop simplification rules based on realistic thermal imaging resolution. T2 - Wind Energy Science Conference (WESC) 2023 CY - Glasgow, Scotland DA - 23.05.2023 KW - Non-destructive testing KW - Thermography KW - Wind turbine blade PY - 2023 AN - OPUS4-58498 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - In situ characterisation of GFRP shell structures of wind turbine rotor blades under test using lock-in thermography N2 - The work shown demonstrates the possibility of measuring the load distribution of complex components such as rotor blades in cyclic tests using thermography. This is confirmed in the experiments presented by comparison with DIC measurements. The advantage of thermography is that it does not require any treatment of the test specimens in the setup shown and the measurement procedure can in principle be scaled to large components. In addition, compared to other imaging methods, the actual loads and not the deformation are measured. With a suitable data evaluation by means of Lockin analysis, small loads can be verified in a formative manner. Using the example of model repairs in shell test specimens made of sandwich glass fibre composite material, it is shown that inhomogeneous load distribution due to internal structures can be detected using thermography. T2 - WindEurope Annual Event 2023 CY - Copenhagen, Denmark DA - 25.04.2023 KW - Thermography KW - TSA PY - 2023 AN - OPUS4-58500 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Infrared Thermography of wind turbine rotor blades: 3 use cases N2 - Thermography is a widely accepted non-invasive measurement method and is generally used in various areas of the life cycle of infrastructure and machinery. This includes design, production and maintenance. Thermography is particularly suitable for remote inspection of large areas that are difficult to reach. In this presentation, applications of thermography in the field of wind energy will be shown, touching on three explicit examples from rotor blade inspection. Experimental testing and validation: Thermography can measure and visualise the stress distribution on the surface during cyclic tests of rotor blades and rotor blade sections. The so-called thermoelastic stress analysis makes use of special evaluation algorithms (Lockin analysis) of the measurement data and the cyclic loading of components. An advantage of the measurement methods compared to other measurement methods such as strain gauges or digital image correlation is that it does not require any extra treatment or sensoring of the components. In the work shown here, repair methods are optimised and evaluated in cyclic tests on shell test specimens. Operation and maintenance: Rotor blades can be inspected from the ground during operation using passive thermography. Here, the integration of weather forecasts and conditions as input for simulations is crucial and will be demonstrated with some examples from the field. The goal of the ongoing research is to obtain detailed insights into the internal structure of the inspected rotor blades with individual measurements. A specially developed automated measuring system is able to measure a wind turbine (one-sided) within 5 minutes without impacting its operation. Environmental impact: In cases where less strict time and economic constraints apply than in the maintenance of rotor blades in operation, thermography can also be used to realise other inspection processes that take more time. Examples of this are quality control or the characterisation of rotor blades during dismantling. In the latter case, for example, it can be crucial to know which components such as foam, balsa, belt and spar are present in which parts of the blade when dismantling the rotor blades. Long-term measurements (~1-2 h) under suitable weather conditions can provide good insights into the inner structure of the rotor blades, both during disassembly and during quality control before installation. For this purpose, the sun is used as a heat source, which induces a thermal response of the rotor blades. The thermal behaviour of the rotor blades then allows conclusions to be drawn about the internal structure. T2 - EERA DeepWind conference 2023 CY - Trondheim, Norway DA - 18.01.2023 KW - Thermography KW - Wind turbine blade KW - Inspection PY - 2023 AN - OPUS4-58501 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael A1 - Krankenhagen, Rainer T1 - EvalTherm - Weather-dependent passive thermography of unheated wind turbine blades N2 - Evaluation of passive Thermography for the inspection of wind turbine blades. Comparison of passive thermography from the ground with drone-supported images and active thermography. Better understand the influence of weather conditions through field measurements. Development of an inspection planning tool that incorporates weather forecasts. Use FEM simulations to predict thermal contrasts of different damages under different environmental conditions. T2 - Kolloquium CY - Saarbrücken, Germany DA - 28.09.2022 KW - Thermography KW - Wind turbine rotor blades KW - FEM PY - 2022 AN - OPUS4-56913 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lay, Vera T1 - Innovations in cemented waste handling and pre-disposal storage (WP7 project PREDIS) N2 - During the webinar "Dismantling and waste management: a playground for innovation" the innovative aspects of the PREDIS work package "innovations in cemented waste handling and pre-disposal storage" are presented. The webinar is jointly organised by Women in Nuclear Decommissioning (WINDI) and Women in Nuclear Innovation (WINI). The shown innovations include quality assurance (mainly NDE) and monitoring technologies with associated sensor systems, digital twin technology, and appropriate data handling including a decision framework. An overview of the on-going research in the field of pre-disposal waste management in the project Predis (WP7) is summarised. T2 - Webinar "Dismantling and waste management: a great playground for innovation" CY - Online meeting DA - 13.10.2022 KW - Predis KW - Waste management KW - Concrete PY - 2022 AN - OPUS4-56926 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin T1 - Introduction to group 8.6 N2 - A brief introduction of group 8.6 and the research work on the application of fiber sensing. T2 - DIU Besuch CY - Berlin, Germany DA - 05.05.2023 KW - Structural health monitoring KW - Distributed fiber sensing KW - Distributed acoustic sensing KW - Practical application PY - 2023 AN - OPUS4-57430 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Chaudhuri, Somsubhro A1 - Stamm, Michael T1 - Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset N2 - With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills. KW - Reference Data KW - Infrared Thermography KW - Wind energy KW - Dataset PY - 2024 DO - https://doi.org/10.5281/zenodo.13771899 PB - Zenodo CY - Geneva AN - OPUS4-61102 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Charmi, Amir T1 - Application of deep learning for structural health monitoring of a composite overwrapped pressure vessel undergoing cyclic loading N2 - Structural health monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite overwrapped pressure vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the digital quality infrastructure initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Anomaly detection KW - Damage localization KW - Deep learning KW - Structural health monitoring KW - Ultrasonic-guided waves PY - 2024 AN - OPUS4-60745 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 - CONF A1 - Niederleithinger, Ernst T1 - PREDIS The pre-disposal management of radioactive waste N2 - This presentation summarizes the opbjectives and the progress of the EURATOM project PREDIS (pre-disposal management of radioactive waste) and its workpackage 7 in particular. Focus is on the development of wireless sensors for monitoring radioactive waste packages, measuring radiation, temperature, pressure and humidity. T2 - DigiDecom 2022 CY - Halden, Norway DA - 18.10.2022 KW - Radioactive waste KW - Monitoring KW - Wireless KW - RFID PY - 2022 AN - OPUS4-56321 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chaudhuri, Somsubhro A1 - Stamm, Michael A1 - Lapšanská, Ivana A1 - Lançon, Thibault A1 - Osterbrink, Lars A1 - Driebe, Thomas A1 - Hein, Daniel A1 - Harendt, René T1 - Infrared Thermography of Turbulence Patterns of Operational Wind Turbine Rotor Blades Supported With High‐Resolution Photography: KI‐VISIR Dataset N2 - With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie—Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTPs) that result from such surface contamination or damage. To complement the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills. KW - Thermography KW - Thermografie KW - Wind energy KW - Leading edge erosion KW - KI PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615772 DO - https://doi.org/10.1002/we.2958 IS - e2958 SP - 1 EP - 9 PB - Wiley AN - OPUS4-61577 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin T1 - Fibre Optic Sensors (Division 8.6) N2 - Fiber optic sensing technology being researched and developed at BAM and its numerous applications/ use cases is presented briefly after a short introductory part on the fundamentals of (distributed) fiber optic sensing. T2 - BAM - RCNDE (UK) Webinar CY - Online meeting DA - 13.11.2024 KW - Distributed fiber optic sensing KW - Structural health monitoring KW - Condition monitoring KW - DAS KW - Embedded fiber optic sensors PY - 2024 AN - OPUS4-61643 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kowalski, Kurt A1 - Hulshoff, S.J. A1 - Ströer, P. A1 - Withag, J. A1 - Genot, A. A1 - Morgans, A.S. A1 - Bake, Friedrich A1 - Venner, K. A1 - Sanders, M.P.J. A1 - Hirschberg, Lionel T1 - Entropy-patch choked-nozzle interaction: quasi-steady-modeling-regime limits probed N2 - Indirect combustion noise due to the interaction of flow inhomogeneities with a choked combustion-chamber exit is an important cause of combustion instability in solid rocket motors. Moreover, it is believed to be an issue in electrical-power generation turbines and aero-engines. If these flow inhomogeneities are essentially characterized by the fluid having a locally appreciablydifferent thermodynamic state, the acoustic response engendered by its interaction with the combustion-chamber exit is commonly referred to as entropy noise. In this paper, dedicated numerical-simulation results of entropy-patch choked-nozzle interactions are presented. Two types of entropy patches were considered: rectangular slugs and circular spots. Moreover, analytical-model-based analysis, of said simulation results, is presented. Based on said analysis, the authors posit the existence of three modeling regimes: the quasi-steady-modeling regime, the blended-physical-effects regime, and the inertial-modeling regime. T2 - 30th AIAA/CEAS Aeroacoustics Conference (2024) CY - Rome, Italy DA - 04.06.2024 KW - Entropy-Nozzle Interaction KW - Indirect Combustion Noise KW - Entropy Noise PY - 2024 SN - 978-1-62410-720-7 DO - https://doi.org/10.2514/6.2024-3113 SP - 1 EP - 23 AN - OPUS4-60174 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Distributed fiber optic sensing for monitoring of underground facilities N2 - Distributed fiber optic sensing (DFOS) technique is a promising and robust non-destructive testing tool that can seamlessly acquire environmental conditions over large scales. Therefore, it has found extensive applications in structural health monitoring. Its appeal for monitoring underground facilities lies in the intrinsic properties of the optical fiber, such as immunity to magnetic interference, small size, chemical inertia, etc. This paper provides a concise overview of DFOS applications in underground facility monitoring. Following a brief introduction to the workingbnprinciple of the DFOS technique, various examples are provided to demonstrate how distributed fiber sensors contribute to monitoring underground facilities. The paper presents unpublished field test results with an emphasis in the energy sector, including monitoring gas storage facilities, geothermal reservoir exploration, and ground movement detection. Furthermore, the paper identifies several directions for enhancing the DFOS system. T2 - SPIE Photonics Europe CY - Strasbourg, France DA - 07.04.2024 KW - Structure health monitoring KW - Geothermal borehole KW - Distributed fiber optic sensing KW - Ground movement KW - Leakage detection PY - 2024 DO - https://doi.org/10.1117/12.3023571 SN - 0277-786X VL - 13001 SP - 1 EP - 10 PB - SPIE AN - OPUS4-60384 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Barretto, T. A1 - Gentes, S. A1 - Braun, J. A1 - Averin, Anton A1 - Lecompagnon, Julien A1 - Stroncik, N. T1 - Automated non-destructive internal corrosion detection on radioactive drums (ZIKA) N2 - The aim of the ZIKA research project, funded by the BMBF funding program FORKA (FKZ:15S9446 A-C), is the automated detection of internal corrosion of radioactive drums using non-destructive testing (NDT). The newly gained findings will be combined with research results from the previous project EMOS (FKZ:15S9420), which dealt with the external damage of drums. Using NDT, internal corrosion and possible internal sources of damage can be identified before they become a safety-relevant issue. However, if internally sourced damages can be seen externally, the integrity of the damaged drum is no longer guaranteed, which has significant consequences. Therefore, early detection before integrity failure is of particular importance for interim storage facilities with low- and medium-level radioactive waste drums. T2 - Kerntechnik 2024 CY - Leipzig, Germany DA - 11.06.2024 KW - Corrosion detection KW - Non-destructive testing KW - Automated inspection system PY - 2024 SP - 1 EP - 5 AN - OPUS4-60322 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Saeed, Hasan A1 - Chaudhuri, Somsubhro A1 - De Waele, Wim T1 - Experimental evaluation of the short and long fatigue crack growth rate of S355 structural steel offshore monopile weldments in air and synthetic seawater N2 - Welded steel structures used in the offshore wind industry are exposed to harsh marine environments, which can result in corrosion-induced fatigue damage. Of particular concern is the heat affected zone (HAZ) of welded joints, a region known for its altered microstructure and mechanical properties, which can significantly influence the initiation and propagation of fatigue cracks. This study investigates the short and long fatigue crack growth rates, and the effect of seawater exposure, for the HAZ in S355 steel weldments. Single-edge notch bend (SENB) specimens are used, with a shallow notch in the HAZ. A series of specimens is immersed in synthetic seawater that is continuously circulated at a controlled temperature to assess the synergistic effects of corrosion and fatigue. The experimental method integrates a novel application of front face strain compliance for monitoring short cracks, alongside an extended back-face strain compliance approach for monitoring long crack propagation. It is concluded that the short fatigue crack growth rate of the HAZ is 2.7 to 3.5 times higher in seawater as compared to air. As the crack propagates and enters into the long crack regime, the ratio decreases to 2.2 times at the transition point of the two-stage crack growth curve and further decreases to 1.5 times when the notch advances towards fracture. The findings indicate that the fatigue crack growth rates documented in standards tend to be on the conservative side. This study significantly enriches the fatigue crack growth data available in literature, which will contribute to a more accurate lifetime assessment offshore wind turbine structures. KW - Fatigue crack growth rate KW - Short crack KW - Offshore monopile foundation KW - Corrosion fatigue PY - 2024 DO - https://doi.org/10.1016/j.apor.2024.104063 VL - 149 SP - 1 EP - 17 PB - Elsevier Ltd. AN - OPUS4-60691 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mehri Sofiani, F. A1 - Tacq, J. A1 - Elahi, S.A. A1 - Chaudhuri, Somsubhro A1 - De Waele, W. T1 - A hybrid probabilistic-deterministic framework for prediction of characteristic size of corrosion pits in low-carbon steel following long-term seawater exposure N2 - A hybrid deterministic-probabilistic framework is presented that combines in-situ measurements and numerical analysis to predict the characteristic size of corrosion pits over extended periods of exposure to seawater. A probabilistic model, informed by corrosion current density and temperature data, applies a sampling method to a range of overpotential values to reflect the surface deterioration in terms of pitting. The model is calibrated and validated with pit size data obtained from corroded coupons. Results reveal a positively skewed truncated lognormal distribution of pit depths which implies a higher prevalence of smaller pit depths, corroborated both experimentally and numerically. KW - Pitting corrosion KW - Offshore structures KW - Probabilistic analysis KW - Potentiodynamic polarisation PY - 2024 DO - https://doi.org/10.1016/j.corsci.2024.112039 VL - 232 SP - 1 EP - 15 PB - Elsevier Ltd. AN - OPUS4-60693 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - Passive infrared thermography as an inspection tool for operational wind turbine rotor blades N2 - The growing wind energy infrastructure presents a significant challenge in the maintenance and operation of wind turbines (WT) and their intricate components. An important aspect of WT maintenance is the inspection of wind turbine rotor blades (WTB) to ensure the overall health and safety of the turbine. This inspection process involves both visual and mechanical examinations of the blades to identify any indicators of damage or wear that could compromise their performance and, consequently, the structural integrity of the entire WT system. The complexity of WTBs is compounded by their ever-expanding dimensions, exceeding 100 meters in length for 16 MW WT systems, and their multi-material composition. Within this context, passive infrared thermography emerges as a potential alternative to conventional contact- or proximity-based inspection methods. Unlike active thermography, passive thermography uses solar radiation and ambient temperature variation for thermal contrast, eliminating the need for traditional heat lamps, flash, or laser-based techniques. A novel inspection method has been developed to semi-autonomously assess wind turbine blades (WTBs) while the wind turbine (WT) is operational, from ground level. This approach leverages optimal thermal contrast, which depends on prevailing weather conditions during field measurements, enabling the visualization of both external and internal features of the WTBs through post-processing techniques. In this study, thermal data obtained through passive thermography is compared with contemporaneous visual imagery to definitively classify observed features in thermal images as either surface or sub-surface features. This analysis, coupled with corresponding weather conditions, provides valuable insights into the capabilities and limitations of the inspection technique. Additionally, finite-element-based (FE) thermal simulations of a WTB section are employed to parametrically assess the influence of weather conditions, beyond those observed during field measurements, based on a validated model. In addition, the thermal images also consist of thermal signatures of leading-edge turbulence due to possible leading-edge erosion in WTBs. These are primarily vortices, and their shape and size depend on the morphology of the damage as well as the rotational speed of the WTBs. The inspections are accompanied by automatic data evaluation of the thermal signatures. To improve the precision of erosion damage identification, a fully convolutional network (FCN) is employed, trained, and tested using over 1000 annotated thermographic blade images. Additionally, the study introduces strategies for grouping smaller damage indications and simplification rules based on realistic thermal imaging resolutions. As leading-edge erosion could potentially lead to annual energy production (AEP) losses, this technique could prove to be a powerful tool in establishing the presence of damage and the resulting AEP loss. T2 - 20th World Conference on Non-Destructive Testing CY - Incheon, South Korea DA - 27.05.2024 KW - Non-destructive testing KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie Anlage Rotorblätter KW - Thermography PY - 2024 AN - OPUS4-60695 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Eisermann, R. A1 - Esterl, F. A1 - Schuchardt, M. A1 - Breithaupt, Mathias A1 - Plath, R. T1 - Distributed acoustic/vibration sensing: Towards advanced transformer condition monitoring N2 - A novel method for transformer vibration monitoring using intensity-based distributed acoustic/vibration sensing (DAS/DVS) is presented. Fibre optic sensors were fully integrated into an oil-filled medium voltage distribution transformer. Opencircuit in-situ measurements were carried out using a single and three-phase supply with an electrical power of up to 30 kVA. Spectral analysis of the mechanical vibrations were performed by measuring the frequency response of the transformer using plain fibre- and transducer-based DVS approaches. The results were compared with the spectra of a microphone mounted on the outside of the transformer housing and the electrical measurements on the high voltage side. Using the fibre optic DVS based monitoring we were able to detect changes in operating power and frequency. Further it could be shown, that it is possible to identify anomalous behaviour such as electrical/mechanical noise sources using the determined vibration spectra. T2 - 2024 IEEE International Conference on High Voltage Engineering and Applications (ICHVE) CY - Berlin, Germany DA - 18.08.2024 KW - Distributed acoustic sensing KW - DAS KW - Transformer monitoring PY - 2024 DO - https://doi.org/10.1109/ICHVE61955.2024.10676084 SP - 1 EP - 4 PB - IEEE CY - New York City, New York, USA AN - OPUS4-61798 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - RPRT A1 - Krankenhagen, Rainer A1 - Chaudhuri, Somsubhro A1 - Stamm, Michael A1 - Lapšanská, Ivana A1 - Aderhold, J. A1 - Schlüter, F. T1 - EvalTherm – Evaluierung der passiven Thermografie für die Zustandsbewertung von Rotorblättern an Windenergieanlagen N2 - Der Bericht gibt einen Überblick über im Rahmen des Projektes durchgeführte Forschungsarbeiten sowie ausgewählte Ergebnisse. Er wurde zusammen mit dem FhI für Holzforschung (WKI) erstellt. KW - Rotorblattinspektion KW - Passive Thermografie KW - Feldmessungen KW - Windturbine PY - 2025 N1 - Schlussbericht des Projektes N1 - Laufzeit: 1.9.2020 – 31.8.2024 N1 - Das Verbundprojekt wurde im Rahmen des 7. Energieforschungsprogramms "Innovationen für die Energiewende" gefördert N1 - Das Vorhaben wurde mit Mitteln des Bundesministeriums für Wirtschaft und Klima unter dem Förderkennzeichen 03EE3035A/B gefördert SP - 1 EP - 67 PB - Technische Informationsbibliothek (TIB) CY - Hannover AN - OPUS4-62647 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro A1 - Junker, Birgit T1 - Infrared thermography as an inspection tool for wind turbine rotor blades N2 - With the rapid expansion of wind turbine construction, rotor blades have increasingly been identified as a primary cause of turbine downtime and failure. This has led to a growing demand for inspection technologies through which energy production losses can be minimised—particularly in comparison to conventional methods such as rope-access inspections—and through which sub-surface blade conditions can be assessed to detect damage or failure in advance. Infrared thermography has been considered as a promising non-contact, full-field inspection method that can be applied to rotor blades in both operational and idle states. In this presentation, delivered at BladesEurope 2025, the physical mechanisms that give rise to thermal contrast during thermographic inspection were explained, as this contrast enables the detection and visualisation of structural features. Results obtained from a collaborative project between BAM and Statkraft Norway were presented, followed by the validation of a finite element simulation through experiments conducted in a climate chamber.” T2 - BladesEurope Forum 2025 CY - Edinburgh, Scotland DA - 29.04.2025 KW - Thermography KW - Wind energy KW - Inspection KW - Windenergie anlage rotorblätte KW - Damage detection PY - 2025 AN - OPUS4-63060 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael A1 - Sridaran Venkat, Ramanan T1 - Harnessing the power of thermal imagery and visual inspection- a mean for reliable damage detection of wind turbine rotor blades N2 - Generation of green electricity as part of the energy transition is leading to a growing market in the wind energy sector all over the world. Maintenance and inspection are key to the reliability, safety and efficiency of wind turbines, the regular maintenance of rotor blades focuses on damage such as erosion on the leading edge of the profile, delamination and thermal cracks due to lightning strikes. To date, visual inspection by technicians (climbers) has been the state of the art and it is time consuming besides posing safety risk for themselves. Recently, drone-based inspections using visual cameras have become more common, enabling fast, reliable and cost-effective inspections. However, no internal damage to the rotor blades can be detected during such an inspection. Thermography is a recognised method for detecting damage beneath the surface of an object, which has been promoted and further developed at BAM for years. To enhance the accuracy and reliability of wind turbine blade inspection, the fusion o T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Wind rotor blade inspection KW - Thermography KW - Data fusion KW - Drone inspection KW - Multi-sensors PY - 2024 AN - OPUS4-62515 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Stamm, Michael T1 - AI-Assisted thermographic and visual classification of leading-edge erosion of wind turbine blades N2 - The wind industry is crucial for carbon neutrality, with turbines featuring blades over 100 meters long. Regular inspections, often manual and visual, struggle to capture subsurface damage or airflow dynamics. Leading-edge erosion, caused by rain and hail, significantly reduces turbine efficiency. The Federal Institute for Materials Research and Testing (BAM) in Berlin is working with industry partners to classify leading-edge damage and estimate yield loss using ground-based thermographic images. These images visualize airflow disruptions caused by erosion. AI models, trained on 1500 thermographic images, can detect and classify this damage. BAM aims to create a reference dataset by 2024, using data from 30 wind turbines. This dataset will include simultaneous thermographic and high-resolution visual images. The project also explores predicting stall and calculating performance loss due to erosion. A secure data platform facilitates data exchange and federated learning, enhancing AI systems with diverse data. KW - NDT KW - Thermography KW - Wind Turbine Blades KW - AI KW - KI-VISIR PY - 2024 UR - https://source.asnt.org/226h005/ SN - 0025-5327 VL - 82 IS - 6 SP - 14 EP - 15 AN - OPUS4-62452 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Strangfeld, Christoph A1 - Müller-Vahl, H. F. A1 - Nayeri, C. N. A1 - Paschereit, C. O. A1 - Greenblatt, D. T1 - Airfoil synchronous surging and pitching N2 - Combined surging and pitching of an airfoil at the identical frequency (i.e. synchronously), at four different phase differences, was investigated theoretically and experimentally. The most general unsteady theoretical formulation was adopted to calculate the lift coefficient, and then extended to explicitly compute the unsteady bound vortex sheet. This was used for comparison with experiments and facilitated the computation of both Joukowsky and impulsive-pressure lift contributions. Experiments were performed using a symmetric 18 % thick airfoil in an unsteady wind tunnel at an average Reynolds number of 3.0*10^5 , with a free-stream oscillation amplitude of 51 %, an angle-of-attack range of 2° +- 2° and a reduced frequency of 0.097. In general, excellent correspondence was observed between theory and experiment, representing the first direct experimental validation of the general theory. It was shown, both theoretically and experimentally, that the lift coefficient was not accurately represented by independent superposition of surging and pitching effects, due to variations in the instantaneous effective reduced frequency not accounted for during pure pitching. Deviations from theory, observed at angle-of-attack phase leads of 90° and 180°, were attributed to bursting of separation bubbles during the early stages of the acceleration phase. The largest deviations occurred when the impulsive-pressure lift contribution was small relative to the Joukowsky contribution, because the latter was most affected by bubble bursting. Bubble bursting resulted in large form-drag oscillations that occurred at identical phase angles within the oscillation cycle, irrespective of the phase difference between surging and pitching, as well as in the absence of pitching. KW - Unsteady aerodynamics KW - Dynamic stall KW - Separation bubble KW - Bubble bursting PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630068 DO - https://doi.org/10.1017/jfm.2025.220 SN - 1469-7645 VL - 1009 SP - 1 EP - 33 PB - Cambridge University Press (CUP) AN - OPUS4-63006 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Brunner, Nanine T1 - Digital Certificates: Enabling Automation in Quality Assurance and Metrological Traceability N2 - This presentation describes how the Digital Calibration Certificate (DCC) for temperature was implemented in the accredited calibration laboratory at BAM. It includes an overview of the integration of the digital calibration symbol (digital seal) to ensure authenticity, integrity, and traceability of the certificate. The DCC is provided in a structured XML format, enabling machine-readability and seamless data integration into digital processes. A demonstrator developed within the QI-Digital project is presented to showcase the practical advantages of the DCC over traditional paper or PDF-based certificates — including automation, data security, and improved usability in digital quality infrastructure systems. T2 - Sensor and Measurement Science International (SMSI) CY - Nuremberg, Germany DA - 05.05.2025 KW - DCC KW - QI Digital KW - Demonstrator KW - SMSI in Nürnberg 2025 PY - 2025 AN - OPUS4-63100 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Nerger, Deborah T1 - Impact damage characterization at RC plates with planar tomography and FEM N2 - Prediction of dynamic effects of reinforced concrete structures under impact loading is a technical challenge. This is a consequence of the great variability of the physical properties resulting from the wide adaptability of reinforced concrete and a consequence of the wide range of impact loading. Experiments and numerical investigations are normally used on a small scale to address the problem. In this paper, impact tests on reinforced conrete plates with the lateral dimensions of 1.5 m x 1.5 m and a thickness of 30 cm are presented. In bending reinforcement, besides the velocity two properties are varied, the diameter and the spatial distribution of the rebars. Experiments are performed at the Otto-Mohr-Laboratory of the Institute of Concrete Structures of the Technische Universit¨at Dresden. Due to the accelerated fall of the impactor the velocity ranges between 20 and 70 m/s. In addition to the measured quantities such as bearing forces, accelerations are also measured at 4 different positions on and under the plate, as well as the deflection at several positions. The measured data are used for the analysis of the damage form and the numerical examinations with the program Ansys Autodyn and the material model after Drucker-Prager. Numerical investigations support the tests, with detailed analysis of individual effects. These numerical computations and the planar tomographic investigations were carried out at BAM in Berlin. With the help of planar tomographic evaluation, the damaged structure is made visible and compared with the numerical results. Influences of the bending reinforcement are explained on the basis of damage evaluation in the local area and on selected measured values. In addition to the test evaluation, the tomographic and numerical methods are presented. T2 - XI International Conference on Structural Dynamics (EURODYN 2020) CY - Online meeting DA - 23.11.2020 KW - Post-impact evaluation KW - Damage characterization KW - Planar tomography KW - Drucker-Prager KW - Ansys Autodyn PY - 2020 AN - OPUS4-51768 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin T1 - KI, Machine Learning und faseroptische Sensorik N2 - Die Verwendung von Methoden des Maschinellen Lernens (ML) und der Künstlichen Intelligenz (KI) im Fachbereich 8.6 Faseroptische Sensorik wird dargestellt. Die vielfältigen Möglichkeiten, Machine Learning auf Basis Künstlicher Neuronaler Netze (ANN) für eine schnelle und effiziente Datenverarbeitung eizusetzen werden demonstriert. Hierfür werden Beispiele für die Anwendungszwecke Messgrößenberechnung, Entrauschen, Interpolation, Bildverarbeitung und Messdatenauswertung aufgezeigt. T2 - Foresight Prozess AM, AP 4, Workshop „In Situ Sensorik“ CY - Online-Meeting DA - 09.11.2020 KW - Verteilte faseroptische Sensorik KW - KI KW - Künstliche Intelligenz KW - Machine Learning KW - Künstliche Neuronale Netze PY - 2020 AN - OPUS4-51545 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Thiele, Marc T1 - Detection and monitoring of the fatigue crack growth on welds – Application-oriented use of NDT methods N2 - Early detection of fatigue cracks and accurate measurements of the crack growth play an important role in the maintenance and repair strategies of steel structures exposed to cyclic loads during their service life. Observation of welded connections is especially of high relevance due to their higher susceptibility to fatigue damage. The aim of this contribution was to monitor fatigue crack growth in thick welded specimens during fatigue tests as holistically as possible, by implementing multiple NDT methods simultaneously in order to record the crack initiation and propagation until the final fracture. In addition to well-known methods such as strain gauges, thermography, and ultrasound, the crack luminescence method developed at the Bundesanstalt für Materialforschung und -prüfung (BAM), which makes cracks on the surface particularly visible, was also used. For data acquisition, a first data fusion concept was developed and applied in order to synchronize the data of the different methods and to evaluate them to a large extent automatically. The resulting database can ultimately also be used to access, view, and analyze the experimental data for various NDT methods. During the conducted fatigue tests, the simultaneous measurements of the same cracking process enabled a comprehensive comparison of the methods, highlighting their individual strengths and limitations. More importantly, they showed how a synergetic combination of different NDT methods can be beneficial for implementation in large-scale fatigue testing but also in monitoring and inspection programs of in-service structures - such as the support structures of offshore wind turbines. T2 - Fatigue Design 2023 CY - Senlis, France DA - 29.11.2023 KW - Crack growth KW - Fatigue KW - NDT KW - Welded PY - 2023 AN - OPUS4-62374 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heimann, Jan T1 - How structural health monitoring can be embedded in a digital quality infrastructure: an example. N2 - The digital Quality Infrastructure (QI) initiative “QI-Digital” in Germany is focusing on implementing new technologies and approaches to ensure that the task of quality assurance is more efficient and ready for the digital and green transformation of the economy. The implementation of quality control key elements, such as Smart Standards, Digital Certificates and QI-cloud solutions shall contribute to solving the socio-economic, ecological, and technological challenges of our time. Hydrogen is a key energy carrier and has the potential to play a significant role in the energy transition, especially in mobility. An essential factor for the broad acceptance of hydrogen-based mobility is the availability of refueling stations that operate reliably and safely. Using the example of a Hydrogen Refueling Station (HRS) built within the QI-Digital initiative, the Federal Institute for Material Research and Testing (BAM) aims to establish a real laboratory where modern measurement techniques and new digital methods are implemented to enhance operational safety, availability, and economic efficiency and render the technology more attractive for the industry. In this work, we present an approach to establish a Structural Health Monitoring (SHM) system on a high-pressure buffer inside HRS and show how it could be embedded into a digital QI. The high-pressure buffers are essential components of the plant which are currently inspected periodically without regard to their operating history. Focusing on the transition to a continuous and digitally supported monitoring of the component’s integrity during operation the novel inspection scheme will be linked to a completely digitalized component-related documentation and tested using digital certificates. This allows the operational safety and, if necessary, the remaining useful lifetime to be assessed on an ongoing basis and to be a valuable contribution to increasing sustainability. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Digital transformation KW - Industry 4.0 KW - Quality control KW - Quality infrastructure KW - Reliability KW - Structural health monitoring PY - 2024 AN - OPUS4-61484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Krebber, Katerina T1 - Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors N2 - This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area. KW - Distributed fiber optic sensors KW - BOTDA KW - BOFDA KW - Machine learning KW - Strain and temperature measurements KW - Artificial neural networks PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580145 DO - https://doi.org/10.3390/s23136187 VL - 23 IS - 13 SP - 1 EP - 26 PB - MDPI AN - OPUS4-58014 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -