TY - CONF A1 - Johann, Sergej A1 - Stührenberg, Jan A1 - Tandon, Aditya A1 - Dragos, Kosmas A1 - Bartholmai, Matthias A1 - Strangfeld, Christoph A1 - Smarsly, Kay T1 - Implementation and validation of robot-enabled embedded sensors for structural health monitoring N2 - In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, facilitating the execution of repair and maintenance work in the early stages of structural damage. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate data relevant to SHM. A promising method for SHM is to embed sensors directly into concrete for recording SHM data from inside structural elements. In this paper, a sensor system for embedment into concrete is proposed, able to assess SHM data recorded from concrete. Power is supplied to the sensors on-demand by quadruped robots, which also collect the SHM data via radio-frequency identification (RFID), providing an automated and efficient SHM process. In laboratory experiments, the capability of the sensor system of automatically collecting the SHM data using quadruped robots is validated. In summary, the integration of RFID technology and robot-based inspection presented in this study demonstrates a vital approach to evolve current SHM practices towards more digitalized and automated SHM. T2 - 11th European Workshop On Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Structural health monitoring KW - RFID-based sensors KW - Smart sensors KW - Embedded sensors KW - Legged robots KW - Quadruped robots PY - 2024 AN - OPUS4-60336 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Johann, Sergej A1 - Stührenberg, Jan A1 - Tandon, Aditya A1 - Dragos, Kosmas A1 - Bartholmai, Matthias A1 - Strangfeld, Christoph A1 - Smarsly, Kay T1 - Implementation and validation of robot-enabled embedded sensors for structural health monitoring N2 - In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, facilitating the execution of repair and maintenance work in the early stages of structural damage. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate data relevant to SHM. A promising method for SHM is to embed sensors directly into concrete for recording SHM data from inside structural elements. In this paper, a sensor system for embedment into concrete is proposed, able to assess SHM data recorded from concrete. Power is supplied to the sensors on-demand by quadruped robots, which also collect the SHM data via radio-frequency identification (RFID), providing an automated and efficient SHM process. In laboratory experiments, the capability of the sensor system of automatically collecting the SHM data using quadruped robots is validated. In summary, the integration of RFID technology and robot-based inspection presented in this study demonstrates a vital approach to evolve current SHM practices towards more digitalized and automated SHM. T2 - 11th European Workshop On Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Structural health monitoring KW - RFID-based sensors KW - Smart sensors KW - Embedded sensors KW - Legged robots KW - Quadruped robots PY - 2024 SP - 1 EP - 8 AN - OPUS4-60335 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wosniok, Aleksander A1 - Königsbauer, Korbinian A1 - Krebber, Katerina A1 - Schaller, M.-Barbara A1 - Färber, Jan A1 - Nöther, Nils T1 - Distributed polymer optical fiber sensors using digital I-OFDR for geotechnical infrastructure health monitoring N2 - We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geosynthetics which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geosynthetics-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Digital I-OFDR KW - Distributed polymer optical fiber sensor KW - Distributed strain sensing KW - Smart geosynthetics KW - Structural health monitoring PY - 2024 AN - OPUS4-60308 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wosniok, Aleksander A1 - Schukar, Marcus A1 - Wang, Bin A1 - Breithaupt, Mathias A1 - Kriegsmann, Andreas A1 - Woody, Paul T1 - Distributed fiber optic strain sensing for structural health monitoring of 70 MPa hydrogen vessels N2 - We report on the development and testing of 70 MPa hydrogen pressure vessels with integrated fiber optic sensing fibers for automotive use. The paper deals with the condition monitoring of such composite pressure vessels (CPVs) using the optical backscatter reflectometry (OBR) applied for a distributed fiber optic strain sensing along fully integrated polyimide-coated single-mode glass optical fiber (SM-GOF). The sensing fibers were embedded into the vessel structure by wrapping them over the polymer liner during the manufacturing process of the carbon fiber reinforced polymer (CFRP). Detecting local strain events by the integrated fiber optic sensors can be an opportunity for monitoring the material degradation of CPVs under static and cyclic loading. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Fiber optic sensor KW - Distributed strain sensing KW - Composite pressure vessel KW - Structural health monitoring KW - Fiber-reinforced plastics PY - 2024 AN - OPUS4-60307 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wosniok, Aleksander A1 - Schukar, Marcus A1 - Woody, Paul A1 - Wang, Bin A1 - Breithaupt, Mathias A1 - Kriegsmann, Andreas T1 - Distributed fiber optic strain sensing for structural health monitoring of 70 MPa hydrogen vessels N2 - We report on the development and testing of 70 MPa hydrogen pressure vessels with integrated fiber optic sensing fibers for automotive use. The paper deals with the condition monitoring of such composite pressure vessels (CPVs) using the optical backscatter reflectometry (OBR) applied for a distributed fiber optic strain sensing along fully integrated polyimide-coated single-mode glass optical fiber (SM-GOF). The sensing fibers were embedded into the vessel structure by wrapping them over the polymer liner during the manufacturing process of the carbon fiber reinforced polymer (CFRP). Detecting local strain events by the integrated fiber optic sensors can be an opportunity for monitoring the material degradation of CPVs under static and cyclic loading. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Fiber optic sensor KW - Distributed strain sensing KW - Composite pressure vessel KW - Structural health monitoring KW - Fiber-reinforced plastics PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-603105 SP - 1 EP - 8 PB - NDT.net AN - OPUS4-60310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wosniok, Aleksander A1 - Königsbauer, Korbinian A1 - Nöther, Nils A1 - Färber, Jan A1 - Schaller, M.-Barbara A1 - Krebber, Katerina T1 - Distributed polymer optical fiber sensors using digital I-OFDR for geotechnical infrastructure health monitoring N2 - We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geosynthetics which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geosynthetics-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Digital I-OFDR KW - Distributed polymer optical fiber sensor KW - Ddistributed strain sensing KW - Smart geosynthetics KW - Structural health monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-603096 SP - 1 EP - 7 PB - NDT.net AN - OPUS4-60309 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Heimann, Jan A1 - Duffner, Eric A1 - Charmi, Amir A1 - Schukar, Marcus A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling N2 - Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Acoustic emission KW - Ultrasonic guided waves KW - Fiber optic sensors KW - Hydrogen KW - Pressure vessels KW - Structural health monitoring KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602764 UR - https://www.ndt.net/search/docs.php3?id=29702 SP - 1 EP - 8 PB - NDT.net AN - OPUS4-60276 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Schukar, Marcus A1 - Breithaupt, Mathias A1 - Duffner, Eric A1 - Ulbricht, Alexander A1 - Prager, Jens A1 - Krebber, Katerina T1 - Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing N2 - We report on distributed fiber optic sensing-based monitoring of hydrogen composite overwrapped pressure vessels (COPV) to simultaneously increase the operational lifespan and mitigate maintenance costs. Our approach represents, to the best of our knowledge, the first application of distributed fiber optic sensing for COPV Type IV monitoring, where the sensing fibers are attached to the surface, rather than integrated into the composite material. Specifically, we attach an optical fiber of 50 m to the pressure vessel's surface, covering both the cylindrical and dome sections. We note that our fiber optic sensing technique relies on swept wavelength interferometry providing strain information along the entire length of the optical fiber with high spatial resolution even at the millimeter scale. When the vessel is pressurized, the sensing optical fiber shows a linear strain response to pressure at every position along the fiber. After thousands of load cycles, the vessel finally fails with the optical fiber detecting and precisely localizing the damage in the vessel’s blind dome area. Furthermore, we discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. This could reduce the number of regular inspections, mitigate premature maintenance costs, and simultaneously increase the vessel’s remaining safe service life. We believe that the structural health monitoring of hydrogen pressure vessels with fiber optic sensors can enhance trust in hydrogen technology contributing to the energy transition in the future. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Hydrogen KW - Fiber optic sensors KW - Composites KW - Machine learning KW - Structural health monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602731 UR - https://www.ndt.net/search/docs.php3?id=29701 SP - 1 EP - 7 PB - NDT.net AN - OPUS4-60273 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Heimann, Jan A1 - Duffner, Eric A1 - Charmi, Amir A1 - Schukar, Marcus A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling N2 - Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Hydrogen KW - Ultrasonic guided waves KW - Fiber optic sensors KW - Acoustic emission KW - Machine learning KW - Pressure vessels KW - Structural health monitoring PY - 2024 AN - OPUS4-60277 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Schukar, Marcus A1 - Breithaupt, Mathias A1 - Duffner, Eric A1 - Ulbricht, Alexander A1 - Prager, Jens A1 - Krebber, Katerina T1 - Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing N2 - We report on distributed fiber optic sensing-based monitoring of hydrogen composite overwrapped pressure vessels (COPV) to simultaneously increase the operational lifespan and mitigate maintenance costs. Our approach represents, to the best of our knowledge, the first application of distributed fiber optic sensing for COPV Type IV monitoring, where the sensing fibers are attached to the surface, rather than integrated into the composite material. Specifically, we attach an optical fiber of 50 m to the pressure vessel's surface, covering both the cylindrical and dome sections. We note that our fiber optic sensing technique relies on swept wavelength interferometry providing strain information along the entire length of the optical fiber with high spatial resolution even at the millimeter scale. When the vessel is pressurized, the sensing optical fiber shows a linear strain response to pressure at every position along the fiber. After thousands of load cycles, the vessel finally fails with the optical fiber detecting and precisely localizing the damage in the vessel’s blind dome area. Furthermore, we discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. This could reduce the number of regular inspections, mitigate premature maintenance costs, and simultaneously increase the vessel’s remaining safe service life. We believe that the structural health monitoring of hydrogen pressure vessels with fiber optic sensors can enhance trust in hydrogen technology contributing to the energy transition in the future. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Hydrogen KW - Composites KW - Pressure vessels KW - Fiber optic sensors KW - Machine learning KW - Structural health monitoring PY - 2024 AN - OPUS4-60275 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ghasem Zadeh Khorasani, Media A1 - Albakri, Bashar A1 - Heimann, Jan 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 - CONF A1 - Karapanagiotis, Christos ED - Krebber, Katerina T1 - Machine learning for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis N2 - We propose to our knowledge for the first time an ensemble of probabilistic machine learning models for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis (BOFDA) applying a two-fiber configuration. T2 - 28th International Conference on Optical Fiber Sensors CY - Hamamatsu, Japan DA - 20.11.2023 KW - Multiparameter sensing KW - Machine learning KW - Fiber optic sensors KW - Structural health monitoring PY - 2023 SP - 1 EP - 4 PB - Optica Publishing Group AN - OPUS4-59397 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Krebber, Katerina T1 - Machine learning for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis N2 - We propose to our knowledge for the first time an ensemble of probabilistic machine learning models for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis (BOFDA) applying a two-fiber configuration. T2 - 28th International Conference on Optical Fiber Sensors CY - Hamamatsu, Japan DA - 20.11.2023 KW - Multiparameter sensing KW - Fiber optic sensors KW - Machine learning KW - Structural health monitoring PY - 2023 AN - OPUS4-59396 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Niederleithinger, Ernst A1 - Epple, Niklas T1 - ridge Monitoring by Ultrasonic Coda Wave Interferometry N2 - The built infrastructure ages and requires regular inspection and, when in doubt, monitoring. To ensure that older concrete bridges showing signs of deterioration can be used safely, several innovative monitoring tools have been introduced, including but not limited to optical, fiber-optic, or acoustic emission techniques. However, there are gaps in the portfolio. A sensing technique that covers a wide range of damage scenarios and larger volumes, while still being sensitive and specific, would be beneficial. For about 15 years, research has been conducted on ultrasonic monitoring of concrete structures that goes beyond the traditional ultrasonic pulse velocity test (PV test), mostly using a very sensitive data evaluation technique called coda wave interferometry. At BAM we have developed sensors and instrumentation specifically for this method. We have instrumented a 70-year-old, severely damaged prestressed concrete bridge in Germany in addition to a commercial monitoring system. We have now collected data for almost 3 years. We can show that we can provide information about the stress distribution in the bridge. We have also been able to confirm that there has been no significant additional damage to the bridge since the installation. T2 - ISNT NDE 2023 CY - Pune, India DA - 07.12.2023 KW - Coda wave interferometry KW - Structural health monitoring KW - Ultrasound KW - Concrete KW - Bridges PY - 2023 AN - OPUS4-59346 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Bartolac, M. A1 - Bien, J. A1 - Górski, M. A1 - Keßler, S. A1 - Küttenbaum, Stefan A1 - Kuzawa, M. A1 - Ley, J. A1 - Maack, Stefan A1 - Mendler, A. A1 - Ryjáček, P. A1 - Santos, L. A1 - Verstrynge, E. ED - Keßler, S. ED - Limongelli, M. P. ED - Apostolidi, E. T1 - Chapter 2: Condition survey - Testing and monitoring methods N2 - The through-life management of our constantly ageing infrastructure is a basic requirement in order to ensure their structural safety and serviceability. Each structure experiences deterioration processes with time leading to a decrease of structural safety and serviceability. The design of new structures considers the expected deterioration for a defined period, the design service life. However, a frequent survey of structural safety controlling structural condition should be mandatory and a maintenance plan should be an integral part of the design. In addition, many structures have exceeded their design service life already or are very close to it leading to an increasing demand for condition assessment. On the one hand, assumptions made during design are not valid any more due to change of the loads, e.g., increasing traffic loads in terms of number and weights. On the other hand, design codes evolved over time in such a way that existing structures do not comply with today’s standards. In all these cases, the through-life management is an important tool to maintain the accessibility of existing structures with known reliability. In line with the new Model Code for Concrete Structures, which includes guidance for both – design of new structures and assessment of existing structures, the Task Group 3.3 focused on the compilation of a state-of-the-art guideline for the through-life management of existing concrete structures, including: Data acquisition by testing and monitoring techniques; Condition assessment for the evaluation of existing structures; Performance prediction using advanced methods; Decision-making procedures to perform a complete assessment of existing structure. The overall objective of the through-life management is the assessment of the current condition and the estimation of the remaining service life under consideration of all boundary conditions. KW - Life management KW - Concrete KW - Non-destructive testing KW - Structural health monitoring KW - State-of-the-art PY - 2023 UR - https://doi.org/10.35789/fib.BULL.0109 SN - 978-2-88394-172-4 DO - https://doi.org/10.35789/fib.BULL.0109.Ch02 SN - 1562-3610 VL - fib Bulletin 109 SP - 16 EP - 38 PB - Fédération internationale du béton (fib) CY - Lausanne AN - OPUS4-59110 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 - 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 - CONF A1 - Wosniok, Aleksander A1 - Königsbauer, Korbinian A1 - Krebber, Katerina A1 - Schaller, M.B. A1 - Färber, J. A1 - Nöther, N. T1 - POF-based monitoring system using digital I-OFDR for strain detection in road construction N2 - We present a prototype of a distributed POF sensing system for strain detection in road construction and civil engineering ready to be used in practical applications. The system is based on a cost-efficient digital incoherent optical frequency domain reflectometry (I-OFDR). In this approach, the strain-induced changes in the Rayleigh backscattering profile along a perfluorinated polymer optical fiber (PF-POF) are determined by measuring the complex transfer function of the sensing fiber using a compact digital data acquisition unit. The digital unit replaces an oversized vector network analyzer (VNA) enabling suitability of the measurement system for out-door use and providing a significant reduction of the total system costs at the same time. The entire sensor con-cept includes the use of geosynthetics with integrated PF-POFs as two-dimensional sensor structures for geotech-nical applications. The robustness and the functionality of the distributed POF I-OFDR sensors were proven in the field. The field test involved embedding of two geomats, each with two polymer optical sensor loops, into the embankment of the federal road B 91 in a section near Leipzig. The measurements conducted after the sensor-based geomats had been installed confirmed both the potential of such smart geosynthetics and the functionali-ty of the digital I-OFDR prototype. T2 - POF2023 CY - Cork, Irland DA - 17.09.2023 KW - Digital I-OFDR KW - Distriubuted polymer optical fiber sensor KW - Smart geosynthetics KW - Strain detection KW - Structural health monitoring PY - 2023 AN - OPUS4-58423 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Krebber, Katerina T1 - Novel fading suppression method for distributed optical fiber acoustic sensing N2 - A universal method based on alternating pulse widths is proposed to suppress the signal fading for all distributed acoustic sensors (DASs) and experimentally demonstrated by one DAS system based on a Mach-Zehnder interferometer. T2 - Optica Sensing Congress CY - Munich, Germany DA - 30.07.2023 KW - Distributed fiber sensing KW - Acoustic sensing KW - Structural health monitoring KW - Fading suppression PY - 2023 AN - OPUS4-58049 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Krebber, Katerina T1 - Novel fading suppression method for distributed optical fiber acoustic sensing N2 - A universal method based on alternating pulse widths is proposed to suppress the signal fading for all distributed acoustic sensors (DASs) and experimentally demonstrated by one DAS system based on a Mach-Zehnder interferometer. T2 - Optica Sensing Congress CY - Munich, Germany DA - 30.07.2023 KW - Distributed fiber sensing KW - Acoustic sensing KW - Fading suppression KW - Structural health monitoring PY - 2023 SP - 1 EP - 2 AN - OPUS4-58048 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -