8.1 Sensorik, mess- und prüftechnische Verfahren
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This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models.
This paper explores combining a gimbal-mounted tunable diode laser absorption spectroscopy (TDLAS) sensor and a video camera to create a virtual gas camera for methane leak detection. This provides a low-to-zero-cost extension of typical TDLAS gas tomography systems. A prototype setup mounted on a ground robot is evaluated. Results acquired using a simulated methane leak show the feasibility of the virtual gas camera, accurately detecting methane leaks by overlaying concentrations onto a visual image. While the acquisition time is significantly longer than for traditional gas cameras, potential enhancements are discussed. The study concludes that the virtual gas camera is feasible and useful, despite its longer acquisition time. It serves as a valuable software-only addition to typical TDLAS gas tomography systems, offering quickly-available on-site data augmentation for visual leak assessment at low-to-zero cost.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional
Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models.
Microalloying elements such as Nb and Ti play a decisive function in achieving the desired mechanical strength of quenched and tempered, high-strength fine-grain structural steels with a nominal yield strength ≥ 690 MPa. The current specifications for the chemical composition only provide manufacturers with upper limits. However, even minor deviations in the alloy concept can have a significant impact on the mechanical properties. Consequently, accurate prediction of weldability and the integrity of welded joints becomes difficult or even impossible due to differences in composition and the resulting microstructures. Undesirable consequences include a possible softening of the heat-affected zone (HAZ) or, conversely, hardening effects. In view of these challenges, various microalloying strategies with different Ti and Nb contents are being systematically investigated for the first time using specially developed laboratory casting alloys. Each alloying route is based on the common S690QL, maintaining both the chemical composition and the heat treatment parameters. To analyse the weldability, three-layer welds were performed using gas metal arc welding (GMAW) and critical microstructures, such as areas of the heat-affected zone (HAZ) that exhibit significant softening or hardening, were identified. The effect of the softened HAZ region on failure was evaluated using transverse tensile specimens. Digital image correlation (DIC) is used to image changes in local strains in different HAZ regions in situ. Using a specially developed mirror system, the local strains of the microstructure zones on the top and bottom of the weld are recorded simultaneously. This makes it possible to analyse how the weld seam geometry (e.g., V-seam) influences the strain gradients. In addition, the analysis of the local deformation helps to understand the effects of the softened HAZ on the global strain, the fracture constriction, the fracture position, and the overall fracture behavior.
With the reduction of large oil spills because of stricter regulations and safety measures, the question of how to manage smaller oil spills arises. Few on-site analytical tools are available for first responders or other law enforcement personnel to rapidly test for crude oil in the early management of localized polluted areas. The approach reported here relies on well-described computer-assisted multivariate data analysis of the intrinsic fluorescence fingerprints of crude oils to build a multivariate model for the rapid classification of crude oils and the prediction of their properties. Thanks to a dedicated robust portable reader, the method allowed classification and accurate prediction of various properties of crude oil samples like density (according to API, the American Petroleum Institute and viscosity as well as composition parameters such as volume fractions of paraffins or aromatics. In this way, autonomous operation in on-site or in-the-field applications becomes possible based on the direct (undiluted and untreated) measurement of samples and a rapid, tablet-operated readout system to yield a robust and simple analytical test with superior performance. Testing in real-life scenarios allowed the successful classification and prediction of a number of oil spill samples as well as weathered samples that closely resemble samples collected by first responders.
The success of this energy transition and the efficient utilization of green hydrogen as a pristine energy carrier hinge upon the cultivation of hydrogen readiness, swift market expansion, and the establishment of crucial value chains within national and European contexts. Paramount to these objectives are reliable quality and safety standards, essential for ensuring supply safety, environmental compatibility, and instilling confidence in these technologies.
In the realm of safety management systems, gas sensors emerge as vital components for detecting hydrogen. This presentation delves into the imperative role of hydrogen systems, emphasizing their capability to monitor and control operations, issue warnings, signal hazardous conditions, and sound alarms. It explores the intricate landscape of functional safety considerations and addresses the challenges associated with measurement uncertainty in sensor testing.
A diverse array of sensors catering to hydrogen, pressure, temperature, and flow are available, each contributing significantly to the safety and efficiency of hydrogen technologies. Applications are envisaged to adhere meticulously to quality infrastructural regulations, codes, and standards, ensuring seamless integration and operation within the broader hydrogen ecosystem.
This discussion underscores the ubiquitous presence of sensor systems across various fields within hydrogen technologies, illustrating their multifaceted contributions to safety, reliability, and overall success in realizing the transformative potential of green hydrogen.
The success of this energy transition and the efficient utilization of green hydrogen as a pristine energy carrier hinge upon the cultivation of hydrogen readiness, swift market expansion, and the establishment of crucial value chains within national and European contexts. Paramount to these objectives are reliable quality and safety standards, essential for ensuring supply safety, environmental compatibility, and instilling confidence in these technologies.
In the realm of safety management systems, gas sensors emerge as vital components for detecting hydrogen. This presentation delves into the imperative role of hydrogen systems, emphasizing their capability to monitor and control operations, issue warnings, signal hazardous conditions, and sound alarms. It explores the intricate landscape of functional safety considerations and addresses the challenges associated with measurement uncertainty in sensor testing.
A diverse array of sensors catering to hydrogen, pressure, temperature, and flow are available, each contributing significantly to the safety and efficiency of hydrogen technologies. Applications are envisaged to adhere meticulously to quality infrastructural regulations, codes, and standards, ensuring seamless integration and operation within the broader hydrogen ecosystem.
This discussion underscores the ubiquitous presence of sensor systems across various fields within hydrogen technologies, illustrating their multifaceted contributions to safety, reliability, and overall success in realizing the transformative potential of green hydrogen.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano
drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano
drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.
Digital twin for hydrogen refueling stations: An analytical study for safe and efficient operations
(2023)
This analytical overview is the initial study in developing the digital sensors twin for the hydrogen refueling station. The research includes a comprehensive analysis of the current literature, refueling protocol, standards, mathematical model, simulation model, and trends in digital twins and H2 technologies. Furthermore, this study has explored the various HRS attributes, such as leakage, temperature, pressure, and hydrogen flow, and identified different use cases of the hydrogen fuel station to build a digital sensor twin. Various real gas equations, such as the Van der Waals and Able-Noble equation of state (EOS), are applied to analyze the key factors and parameters affecting hydrogen and the MATLAB simulation model to validate the key elements and parameters. This study also focuses on identifying and addressing research gaps in hydrogen technology, contributing to advancing hydrogen infrastructure, and supporting the global transition to cleaner energy solutions.
The transfer of data for the metrological traceability of measurement results is currently taking place in the form of paper-based calibration certificates, mostly. Especially accredited metrology services providers are often bound to paper certificates by regulatory constrictions and sometimes lack of IT-infrastructure. This analog form of metrology data transfer, however, implies hands-on work, in particular for the certificate receiver, as its key values need to be translated into the device- and quality management (QM) system, manually. With thousands of certificates transferred from accredited calibration labs until the industrial regime, this binds a significant amount of human resources and is a well-known source of errors, thus constricting cost-effective and secure workflows.
This contribution highlights the status of the advancing digital transformation in metrology, on the example of force calibration. Although this appealing initiative is to a large extend driven by German stakeholders, it currently evolves to extend across all Europe and far beyond.
A safety or security related assessment of explosions, accidental and intentional scenarios alike, often necessitate performance of replication-tests. Such test results are necessary to clarify the causes within the scope of forensic investigations. To gain important insights into the behavior of structures and materials under such loading, field tests may also be performed in accordance with different test standards. To determine the resistance of building-structures after explosions, estimation of the residual load-bearing capacity in addition to the assessment of dynamic structural response and damage to the building components is important. In most cases an evaluation of structural integrity is based only on the visual damage, resulting in an overestimation of the residual capacity.
The Bundesanstalt für Materialforschung und -prüfung (BAM) operates the Test site for Technical Safety (TTS) on an area measuring about 12 km2 in the Federal State of Brandenburg for execution of true-to-scale explosion tests. At the TTS, building component testing was performed to assess the suitability of different non-destructive testing methods to characterize the dynamic structural response and damage resulting from the detonation of high explosives.
Different blast-loading scenarios were realized by varying the net explosive mass and the standoff distance with all scenarios representing a near-field detonation. The test object was a reinforced concrete wall 2 m high, 2.5 m wide and 20 cm thick, fixed at both vertical edges. The dynamic loading of the wall was characterized with 8 piezoelectric pressure sensors flush-mounted on the front surface, thus measuring the reflected pressures from the shock wave. The tests were conducted with the aim of characterizing the global behavior of the wall under dynamic shock loading and the resulting local damage pattern, respectively. High speed digital image correlation was implemented in combination with multiple acceleration sensors to observe the rear surface of the wall to chart the dynamic deflection during the loading and to determine the residual deformation after the loading had ceased. In addition, one test specimen was instrumented with fiber optic sensor cables, both fixed to the rebars and embedded in the concrete-matrix, respectively. Firstly, these sensors were interrogated during the blast test by a distributed acoustic sensing (DAS) device using a particularly high sampling rate to measure the shock-induced vibrations in the structure with high temporal resolution. This delivers information on dynamics of compression and tension cycles from within the structure. Secondly, the local damage-pattern emerging during the series of blasts was determined via distributed fiber optic strain sensing (DSS) by interrogating the embedded fiber optic sensors with a high spatial resolution DSS device after each blast. This enabled the characterization of non-visual damage to the structure, in particular with regard to the formation of localized cracks in the concrete matrix. The DSS was further complimented by a structure-scanner based on ultrasonic measurements.
Our contribution describes this new test approach in detail. Results of the three datasets, namely dynamic shock loading, global behavior of the test object and the local damage pattern will be presented. The suitability of the implemented measurement methods will be discussed in combination with the challenges in their application for technical safety evaluation of building components under explosive loading.
Robotic Measurement System Concept for Greenhouse Gas Tomography in Wastewater Treatment Plants
(2023)
This paper introduces a conceptual framework for a greenhouse gas (GHG) measurement system designed for wastewater treatment plants (WWTPs). The emphasis is on accurate gas tomography measurements of emissions, crucial for national emission assessments. The proposed system is movable, semi-autonomous, and employs a combination of ground and air robots equipped with integral remote gas sensors and in-situ sensors. The proposed two/two configuration ensures comprehensive coverage of WWTPs, overcoming obstacles. The system concept lays the foundation for future implementation and evaluation, addressing the need for enhanced understanding of GHG emissions from WWTPs.
Robotic Measurement System Concept for Greenhouse Gas Tomography in Wastewater Treatment Plants
(2023)
This paper introduces a conceptual framework for a robotic greenhouse gas (GHG) measurement system designed for wastewater treatment plants (WWTPs). The emphasis is on accurate gas tomography measurement of emissions, crucial for national emission assessments. The proposed system is movable, semi-autonomous, and employs a combination of ground and air robots equipped with integral remote gas sensors and in-situ sensors. Employing a two/two configuration ensures comprehensive coverage of WWTPs, overcoming obstacles. The system concept lays the foundation for future implementation and evaluation, addressing the need for enhanced understanding of GHG emissions from WWTPs.
This poster reports an improved outdoor gas source localization algorithm, showing promising results under simulations with a realistic gas plume. In the scenario assumed in this work, an unmanned ground vehicle searches for the location of a gas source in an open outdoor field. The simulated gas plume in this work consists of frequent and large meandering as a real outdoor plume. The source location is estimated using gas and wind measurements obtained from sensors fixed on a mobile platform. When a gas puff is detected, its source is likely to be in the upwind direction. Particle filter-based algorithms have been proposed in previous work to estimate the most likely source location from multiple gas detection events. In this work, the particle weight update function has been modified by adopting a 2D Gaussian plume model, to improve the accuracy in estimating the likelihood of the source location. We have evaluated the performance of this algorithm combined with an estimation-based route planning algorithm. The simulator uses wind data recorded outdoors to calculate transport of gas puffs allowing a formation of a gas plume containing large meandering, due to real fluctuations of outdoor wind. Simulations of the improved particle filter with the estimation-based route planning algorithm have yielded more accurate, stable and time-efficient results than the pre-modified version.
Gas source localization (GSL) helps mitigate the impact of industrial accidents and natural disasters. While GSL may be dangerous and time-consuming when performed by humans, swarms of agile and inexpensive nano aerial robots may increase the safety and efficiency of gas source localizations. Since the small payloads of nano aerial robots limit the sensing and computing resources, strategies adapted from biological swarms, such as colonies of social insects, are used to coordinate robot
swarms. Most swarm GSL strategies are based on the assumption that the maxima of gas concentrations are sufficiently close to the gas sources. However, prior studies have indicated that the occurrence of “bouts”, a metric for the intermittency of gas distributions, may advantageously be used as a more accurate gas source proximity indicator. This paper presents a swarm GSL strategy employing bouts as source proximity indicators and a bio-inspired pheromone system for communication. Nano aerial robots, deployed in this study, act as agents and emit
pheromone markers in an artificial environment upon detecting bouts. Leveraging the concept of artificial potential fields, the agents switch between exploiting the knowledge of the swarm by following pheromone gradients and exploring the search space by targeting a random point. The agents are repelled by each other and by walls to avoid collisions. The swarm GSL strategy is implemented into three nano aerial robots and validated in a real-world experiment in an indoor environment with a single gas source. The results indicate that the the swarm GSL strategy
presented in this paper is capable of GSL in indoor environments and that the intermittency of gas distributions is a better source proximity indicator than the mean concentration.
Gas source localization (GSL) helps mitigate the impact of industrial accidents and natural disasters. While GSL may be dangerous and time-consuming when performed by humans, swarms of agile and inexpensive nano aerial robots may increase the safety and efficiency of gas source localizations. Since the small payloads of nano aerial robots limit the sensing and computing resources, strategies adapted from biological swarms, such as colonies of social insects, are used to coordinate robot swarms. Most swarm GSL strategies are based on the assumption that the maxima of gas concentrations are sufficiently close to the gas sources. However, prior studies have indicated that the occurrence of “bouts”, a metric for the intermittency of gas distributions, may advantageously be used as a more accurate gas source proximity indicator. This paper presents a swarm GSL strategy employing bouts as source proximity indicators and a bio-inspired pheromone system for communication. Nano aerial robots, deployed in this study, act as agents and emit pheromone markers in an artificial environment upon detecting bouts. Leveraging the concept of artificial potential fields, the agents switch between exploiting the knowledge of the swarm by following pheromone gradients and exploring the search space by targeting a random point. The agents are repelled by each other and by walls to avoid collisions. The swarm GSL strategy is implemented into three nano aerial robots and validated in a real-world experiment in an indoor environment with a single gas source. The results indicate that the the swarm GSL strategy presented in this paper is capable of GSL in indoor environments and that the intermittency of gas distributions is a better source proximity indicator than the mean concentration.
Der Einsatz von hochfesten niedriglegierten Stählen (HSLA) ist in vielen Industriesektoren, wie dem allgemeinen Bauwesen, Offshore Strukturen, Mobilkranbau usw. unumgänglich geworden. Die steigende Nachfrage an Stählen mit einer hohen Festigkeit und gleichzeitig hervorragenden Zähigkeit stellt ein ökonomisches und ökologisches Potential dieser Stähle in Vordergrund. Aktuell gültige Normvorgaben (EN ISO 10025-6) geben den Stahlherstellern Vorgaben bezüglich der chemischen Zusammensetzung, um die geforderten mechanischen Eigenschaften erreichen zu können. Die Legierungsrouten variieren jedoch von Hersteller zu Hersteller. In Betracht der Schweißeignung dieser Stähle zeigt sich das Problem, dass diese bereits bei geringen Nb- und Ti-Mikrolegierungsunterschieden im Grundwerkstoff ein divergentes metallurgisches Verhalten in der Wärmeeinflusszone (WEZ) aufweisen.
In diesem Vortrag werden die schweiß-metallurgischen Unterschiede zwischen verschieden mikrolegiertem hochfestem Feinkornbaustahl S690QL thematisiert. Zur Prüfung der Schweißnahtfestigkeit wurden mittels eines instrumentierten DIC (ARAMIS) Systems Querzugversuche an Schweiß-Querflachzugproben durchgeführt.
Monitoring and maintenance of civil infrastructure are of great importance, as any undetected damage can cause high repair costs, unintended deadtime, or endanger structural integrity. The inspection of large concrete structures such as bridges and parking lots is particularly challenging and time-consuming. Traditional methods are mostly manual and involve mapping a grid of measurement lines to record the position of each measurement. Current semi-automated methods, on the other hand, use GPS or tachymeters for localization but still require trained personnel to operate. An entirely automated approach using mobile robots would be more cost- and time-efficient. While there have been developments in using GPS-enabled mobile robots for bridge inspection, the weak signal strength in indoor areas poses a challenge for the automated inspection of structures such as parking lots. This paper aims to develop a solution for the automated inspection of large indoor concrete structures by addressing the problem of robot localization in indoor spaces and the automated measurement of concrete cover and rebar detection.
Monitoring and maintenance of civil infrastructure are of great importance, as any undetected damage can cause high repair costs, unintended deadtime, or endanger structural integrity. The inspection of large concrete structures such as bridges and parking lots is particularly challenging and time-consuming. Traditional methods are mostly manual and involve mapping a grid of measurement lines to record the position of each measurement. Current semi-automated methods, on the other hand, use GPS or tachymeters for localization but still require trained personnel to operate. An entirely automated approach using mobile robots would be more cost- and time-efficient. While there have been developments in using GPS-enabled mobile robots for bridge inspection, the weak signal strength in indoor areas poses a challenge for the automated inspection of structures such as parking lots. This paper aims to develop a solution for the automated inspection of large indoor concrete structures by addressing the problem of robot localization in indoor spaces and the automated measurement of concrete cover and rebar detection.
Digital Calibration Certificates: Transforming Efficiency and Safety in Hydrogen Refuelling Station
(2023)
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.
Packagings for the transport of dangerous goods need to meet special requirements to get an approval. This includes free fall drop testing onto an essentially unyielding surface as a means to assess a package’s resistance to mechanical damage. A main requirement for drop tests is that the impact target’s mass shall be at least 50 times that of the heaviest package to be tested. Nevertheless, many manufacturers do not possess foundation structures with the required mass ratio. Previous evidence highlights that the mass ratio is not a decisive criterion on its own. Parameters such as the impact target foundation’s connection and the impulse experienced by the impacting object are essential as well. However, these factors are not easily verifiable since experimental measurements are not possible at most facilities. The objective of this work is to provide a detailed analysis on the interaction between impact target foundation and subgrade in dynamic impact testing using validated finite-element (FE) models. This research is highly beneficial for industrial application since it allows manufacturers to make informed predictions about the mechanical response of installed impact target foundations.
Occupational health is an important topic, especially in industry, where workers are exposed to airborne by-products (e.g., dust particles and gases). Therefore, continuous monitoring of the air quality in industrial environments is crucial to meet safety standards. For practical and economic reasons, high-quality, costly measurements are currently only carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only.
The project “Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios” (RASEM) addresses this issue by bringing together the benefits of both – low- and high-cost – measuring technologies enabling cost-efficient long-term air quality monitoring in realtime: A stationary network of low-cost sensors that is augmented by mobile units carrying high-quality sensors. By mapping the distribution of gases and particles in industrial environments with the proposed RASEM system, measures can be identified to improve on-site working conditions much faster than using traditional methods.
In this presentation, we detail the technical aspects of RASEM and introduce the mobile platforms used.
Since several years, magnetic sensor elements are available in fully flexible architectures that often reveal identical sensing properties, compared to their rigid counterparts, while being bent severely and repeatedly. Nowadays, a variety of magnetic senor principles, including well known Hall, AMR and GMR but also more exotic PHE and AHE sensors were demonstrated on such flexible platforms. The novel properties of being thin, lightweight, shapeable, and wearable enable magnetic sensory systems to be utilized in vicinities and conditions that are inaccessible for rigid and microchip-based sensors.
Since several years, magnetic sensor elements are available in fully flexible architectures that often reveal identical sensing properties, compared to their rigid counterparts, while being bent severely and repeatedly. Nowadays, a variety of magnetic senor principles, including well known Hall, AMR and GMR but also more exotic PHE and AHE sensors were demonstrated on such flexible platforms. The novel properties of being thin, lightweight, shapeable, and wearable enable magnetic sensory systems to be utilized in vicinities and conditions that are inaccessible for rigid and microchip-based sensors.
QI-Digital is a joined project aiming at digitalising Quality Infrastructure (QI) processes involving standardization, conformity assessment, accreditation, metrology, and market surveillance [1]. Federal institute of material research and testing (BAM) is working on the creation of a digital calibration certifi-cate (DCC) to achieve digital metrological traceability and conformity assessment. The utilisation of machine readable and executable DCCs in the XML format is demonstrated on an example of a tem-perature measurement at a hydrogen refueling station. The certificates will be retrieved and analysed automatically at a Process Control System or at a Digital Twin.
Contamination of natural bodies of water or soil with oils and lubricants (or generally, hydrocarbon derivatives such as petrol, fuels, and others) is a commonly found phenomenon around the world due to the extensive production, transfer, and use of fossil fuels. In this work, we develop a simple system for the on-field detection of total petroleum hydrocarbons (TPHs) in water and soil. The test is based on the measurement of the fluorescence signal emitted by the molecular rotor 2-[ethyl[4-[2-(4-nitrophenyl)ethenyl]phenyl]amino]ethanol (4-DNS-OH). This dye is embedded in a hydrophobic polymeric matrix (polyvinylidene fluoride), avoiding interactions with water and providing a robust support for use in a test strip fashion. Together with the strips, an embedded optical system was designed for fluorescence signal read-out, featuring a Bluetooth low-energy connection to a commercial tablet device for data processing and analysis. This system works for the detection and quantification of TPHs in water and soil through a simple extraction protocol using a cycloalkane solvent with a limit of detection of 6 ppm. Assays in surface and sea waters were conclusive, proving the feasibility of the method for in-the-field operation.
For dangerous goods packagings, drop testing onto an essential unyielding target can be used to assess the mechanical resistance to impact loads. Adopted regulations like ADR/RID require that the impact surface provided shall be integral with a mass at least 50 times than that of the heaviest package to be tested. The problem is that many manufacturers do not possess impact targets that satisfy the required 50 times mass ratio for regulative drop tests during series production. The objective of this work is to verify existing and define improved criteria for impact target structures based on systematic investigations. Previous evidence highlights the relevance of other parameters in addition to the mass ratio. Therefore, in this research, a variation of drop test parameters was carried out experimentally. Furthermore, numerical vibration analysis was applied to investigate the deformability of the impact surface. The results conclude that the mass ratio of 1:50 cannot be defined as a decisive criterion. In order to determine the influence of further drop test parameters, the research findings were used to validate a parametric model which assesses impact target deflection. An approximation quality of over 90 % was achieved. As a result, new evaluation criteria are proposed. Firstly, a method for identifying critical impact target designs is provided. Secondly, a new comprehensive formula compares the approximated maximum deflection of a real impact target to the respective theoretical threshold derived from a worst-case assumption. In practice, this leads to great advantages in the evaluation of already installed impact targets for dangerous goods packagings.
For dangerous goods packagings, drop testing onto an essential unyielding target can be used to assess the mechanical resistance to impact loads. Adopted regulations like ADR/RID require that the impact surface provided shall be integral with a mass at least 50 times than that of the heaviest package to be tested. The problem is that many manufacturers do not possess impact targets that satisfy the required 50 times mass ratio for regulative drop tests during series production. The objective of this work is to verify existing and define improved criteria for impact target structures based on systematic investigations. Previous evidence highlights the relevance of other parameters in addition to the mass ratio. Therefore, in this research, a variation of drop test parameters was carried out experimentally. Furthermore, numerical vibration analysis was applied to investigate the deformability of the impact surface. The results conclude that the mass ratio of 1:50 cannot be defined as a decisive criterion. In order to determine the influence of further drop test parameters, the research findings were used to validate a parametric model which assesses impact target deflection. An approximation quality of over 90 % was achieved. As a result, new evaluation criteria are proposed. Firstly, a method for identifying critical impact target designs is provided. Secondly, a new comprehensive formula compares the approximated maximum deflection of a real impact target to the respective theoretical threshold derived from a worst-case assumption. In practice, this leads to great advantages in the evaluation of already installed impact targets for dangerous goods packagings.
Gas distribution mapping (GDM) is a valuable tool for monitoring the distribution of gases in various applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This presentation gives an overview of the different approaches to GDM and motivate the use of a deep-neural network-based approach. As access to ground truth representations of gas distributions remains a challenge in GDM research, an approach for the simulation of realistic-shaped synthetic gas plumes is described, which was used for training Gas Distribution Decoder, a deep neural network for spatial interpolation of spatially sparse gas measurements.
This tutorial provides an overview of the utilization of sensor systems in hydrogen technologies. It discusses the necessary criteria and characteristics of sensor systems, and also demonstrates practical applications of these technologies in terms of safety and process control. Emphasis is placed on various types of sensors such as gas sensors, pressure sensors, temperature sensors, and gas flow sensors. These systems find application in monitoring and regulating operational parameters, detecting potentially dangerous situations, and activating alarm systems.
QI-Digital is a joined project aiming at digitalising Quality Infrastructure (QI) processes involving standardization, conformity assessment, accreditation, metrology, and market surveillance [1]. Federal institute of material research and testing (BAM) is working on the creation of a digital calibration certifi-cate (DCC) to achieve digital metrological traceability and conformity assessment. The utilisation of machine readable and executable DCCs in the XML format is demonstrated on an example of a tem-perature measurement at a hydrogen refueling station. The certificates will be retrieved and analysed automatically at a Process Control System or at a Digital Twin.
Beschichtete Zellulose Partikel als kolorimetrische passive Sensoren, die mittels optische Kamerasystem einer Drohne erfasst werden. Somit können Gefahrenstoffe, in ersten Versuchen z. B. Säuren aus sicherer Entfernung, kostengünstig und umweltverträglich detektiert werden.
Due to the high damping mechanical properties of fiberboard materials, corrugated fiberboard boxes are widely used as transport packagings of dangerous goods. Since there is risk of vertical impact within the distribution system, drop tests are performed as design type tests to assess a package’s ability to withstand damage. In this context, however, little is known regarding the transient mechanical response of a filled fiberboard box. The reason is that the nature of the packaging material presents challenges in identifying the deformation mechanisms experimentally. The aim of this work is to provide a framework for predicting the damage resistance of complete, filled corrugated fiberboard boxes which are ready for transportation. Therein, drop test finite-element simulations are used and validated by experimental results. These numerical calculation models are a valuable tool for virtual product development and contribute to the optimization of the design cycle.
Setting up sensors for the purpose of environmental monitoring should be a matter of days, but often drags over weeks or even months, preventing scientists from doing real research. Additionally, the newly developed hardware and software solutions are often reinventing existing wheels. In this short paper, we revisit the design of our environmental sensing node that has been monitoring industrial areas over a span of two years. We share our findings and lessons learned. Based on this, we outline how a new generation of sensing node(s) can look like.
Setting up sensors for the purpose of environmental monitoring should be a matter of days, but often drags over weeks or even months, preventing scientists from doing real research. Additionally, the newly developed hardware and software solutions are often reinventing existing wheels. In this short paper, we revisit the design of our environmental sensing node that has been monitoring industrial areas over a span of two years. We share our findings and lessons learned. Based on this, we outline how a new generation of sensing node(s) can look like.
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.
Multi-sensory monitoring and ultrasound for quality assurance at underground sealing structures
(2023)
Within the safety concepts of underground disposal sites of nuclear waste, engineered barriers play an important role. As these sealing structures have high demands concerning integrity, we aim at advancing the available construction materials, monitoring, and inspection techniques within the project SealWasteSafe. A specifically developed alkali-activated material is compared to classical salt concrete. A comprehensive multi-sensory monitoring scheme is used at 150-340 l specimens to monitor setting and hardening of both materials. All sensors are demonstrated to resist the highly alkaline environments. Besides cabled and wireless temperature and humidity of the materials, strain variations using fibre optic sensors and acoustic emissions are recorded over periods of at least 28 days, partly for more than eight months. After hardening of the specimens, further nondestructive evaluations using ultrasonic echo and thermographic measurements are conducted.
Preliminary results proof the suitability of the tested sensors and clearly highlight differences between the tested materials. Particularly, the newly developed alkali-activated material shows lower acoustic emission activity indicating less cracking activity. Additionally, unique ultrasonic methods will enable better images of potential internal objects and cracks at in-situ sealing structures. A largescale ultrasonic system is optimised to reliably detect objects at a depth exceeding 9 m while still obtaining a good resolution. Modelling studies show the potential of further increasing the distance between individual transducer arrays. Additionally, a new ultrasonic borehole probe using phased arrays allowing for beam focussing is constructed and tested. Laboratory measurements at a halfcylindrical concrete specimen coincide well with the previous modelling. In total, the presented safe materials, detailed monitoring approaches and ultrasonic quality assurance methods will help to obtain safe sealing structures within salt as a host rock. The concepts can partly be transferred to sealing structures in alternative host rocks and will also be valuable for non-nuclear waste repositories.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or poorly accessible areas. A challenging task however, is the generation of three-dimensional distribution maps from these spatially sparse gas measurements. To obtain high-quality reconstructions, the choice of optimal measuring poses is of great importance. Remote gas sensors, that are commonly used in Robot Assisted Gas Tomography (RAGT), require reflecting surfaces within the sensor’s range, limiting the possible sensing geometries, regardless of whether the robots are ground-based or airborne. By combining ground and aerial robots into a heterogeneous swarm whose agents are equipped with reflectors and remote gas sensors, remote inter-robot gas measurements become available, taking RAGT to the next dimension – releasing those constraints. In this paper, we demonstrate the feasibility of drone-to-drone measurements under realistic conditions and highlight the resulting opportunities.
In order to detect small and hidden material defects or micro-cracks, weak and localized magnetic stray fields need to be detected and characterized on the magnetic surface. Current flux leakage sensor elements are rigid and rely on air gaps to detect stray fields several hundred micrometers above the surface. Furthermore, the increasing complexity of critical components, enabled by novel fabrication technologies, primarily additive manufacturing, poses a significant challenge to the available flux leakage testing technologies.
The emergence of flexible magneto-electronics paves the way to a compliant form of magnetic sensors that, on one hand, are able to adapt to highly curved surfaces, allowing these measurements to be conducted on irregularly formed components, which are inaccessible for nowadays electromagnetic NDT equipment and, on the other hand, could enhance the detection limit for small magnetic stray fields, by allowing the magneto-sensitive elements to approximate the object’s surface until only a few µm, mediated by thin and flexible packaging.
This work focuses on the design and fabrication of low-cost and fast-response of an electronic nose (E-nose) based on semiconductor gas sensors, for discriminating some synthetic gases such as ammonia (NH3), ethanol (C2H5OH), and hydrogen sulfide (H2S). Additionally, the capability of separating different concentration levels of each considered gases was checked. Dataset treatment of E-nose by using Principal Component Analysis (PCA) showed a good discrimination of the different synthetic gases. Furthermore, perfect classification was reached of different concentration levels of the analysed gases by using Discriminant Function Analysis (DFA). In the light of these results, it could be stated that the developed E-nose system constitutes an inexpensive, rapid, simple to use, and efficient tool for synthetic gases detection.
A steel pipeline segment of 2.5 m length was subjected to quasi-static four-point bending load in three steps for studying the initial cracking and damage accumulation based on the Acoustic Emission (AE) technique and by the direct current potential drop (DCPD) technique. For the latter, a new post-test analysis method was established.
AE is found more sensitive to crack initiation than DCPD. Formation of mesoscopic and macroscopic cracks as well as their closure and the resulting friction generate weighted peak frequencies below 400 kHz, whereas microscopic cracking produces AE with broad band spectra identifiable by weighted peak frequencies above 400 kHz. Critical states alike the maximum load level and the leak opening were accompanied by peak amplitudes above 85 dBAE. This rather fundamental study provides a data base for possibly developing advanced strategies of detection and alarm systems based on acoustic monitoring of pipelines, or at least, steel structures.
In many cases, science falls back on self-developed prototype systems, which are used and developed for the measurement and execution of the tasks. About 80 % of the development is based on the same hardware design, which is used in only one application scenario and then discarded. For the most part, there are also uncalibrated sensors, since it is costly to calibrate a complicated sensor measurement system or the entire measurement chain since access to the sensor systems is not always available. This paper describes a conceptual design to implement a versatile sensor system with the motivation to fuse the data recording and data reception, which can cover large areas with the help of LoRaWAN (Long Range Wide Area Network) technology [1, 2]. To overcome disadvantages of LoRaWAN, namely the slow data rates, the proposed sensor system can also cover smaller areas with the widespread WiFi technology. An enormous advantage over individual complete systems in the form of a prototype, is the rapid expansion, uncomplicated calibration of the individual sensors and the ecological relief.
A modular design is used, where individual stacks with sensors and peripherals can be added separately. The stacks are standalone low-power systems and can be calibrated, maintained, and replaced separately and do not require the entire measurement chain. The measured and sent values are stored locally on the main stack and sent to the data collector (gateway) and evaluated by means of the automated selection between WiFi and LoRaWAN.
In many cases, science falls back on self-developed prototype systems, which are used and developed for the measurement and execution of the tasks. About 80 % of the development is based on the same hardware design, which is used in only one application scenario and then discarded. For the most part, there are also uncalibrated sensors, since it is costly to calibrate a complicated sensor measurement system or the entire measurement chain since access to the sensor systems is not always available. This paper describes a conceptual design to implement a versatile sensor system with the motivation to fuse the data recording and data reception, which can cover large areas with the help of LoRaWAN (Long Range Wide Area Network) technology. To overcome disadvantages of LoRaWAN, namely the slow data rates, the proposed sensor system can also cover smaller areas with the widespread WiFi technology. An enormous advantage over individual complete systems in the form of a prototype, is the rapid expansion, uncomplicated calibration of the individual sensors and the ecological relief.
A modular design is used, where individual stacks with sensors and peripherals can be added separately. The stacks are standalone low-power systems and can be calibrated, maintained, and replaced separately and do not require the entire measurement chain. The measured and sent values are stored locally on the main stack and sent to the data collector (gateway) and evaluated by means of the automated selection between WiFi and LoRaWAN.