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Emissions of ammonia into the environment are mainly caused by agriculture, but also by combustion processes in waste and by road traffic. Even at low concentrations, this substance is not only an odour nuisance, but also a substance with ecological and climatic relevance. Therefore, BAM tested commercial electrochemical, and metal oxide based sensors, which have limited suitability for measuring in the environmental molar fraction range. Alternatively, own developments for the detection of ammonia in the trace range were implemented, wherein the analyte is measured by changing the fluorescence of a BODIPY dye at 550 nm by means of a portable fluorescence sensor directly from the gas phase. For the calibration of ammonia sensors and measuring instruments, a stationary system based on the mixture of certified test gases from pressure cylinders with calibrated mass flow controllers is available. A test gas generator was developed for on-site calibration and testing of sensors and measuring devices. The generation of ammonia-containing gases in the environmental relevant range of levels below 1000 nmol/mol is carried out by the permeation method according to ISO 6145-10. For the traceability of ammonia, standards are provided and further developed by the National Metrological Institutes and designated institutes. Metrological standards are based on SI units and are a basis for traceability of sensors or gas analysers.
Leaking methane from infrastructures, such as pipelines and landfills, is critical for the environment but can also pose a safety risk. To enable a fast detection and localization of these kind of leaks, BAM developed a novel robotic platform for aerial remote gas sensing. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer a number of advantages for inspection and surveillance over traditional methods. No direct contact with the target gas is needed and thus the influence of the copter downwash on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of mini-copters. Here, we present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile multicopter. The proposed system can be deployed in scenarios that cannot be addressed by currently available systems and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). It enables tomographic reconstruction of gas plumes and a localization of gas sources. We also present first results showing its performance under realistic conditions.
Leaking methane (CH4) from infrastructures, such as pipelines and landfills, is critical for the environment but can also pose a safety risk. Nowadays, after a period of failure-free exploitation, a significant increase in fires and explosions due to pipeline leakages, can be observed. Particularly problematic are the gas pipelines in Poland that were built several decades ago. At that time, the technical standards and building regulations were more lenient than those currently being in force. The integrity of the gas pipelines is difficult to inspect as they are not piston-optimized (they were not built for this purpose). To overcome this challenge, the UDT is looking for methods that enable a fast and reliable detection and localization of leaking pipelines over long distances.
The BAM has developed a robotic platform for aerial remote gas sensing that meets the expectations of UDT. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer several advantages for inspection and surveillance over traditional methods. No direct contact with the target gas is needed and thus the influence of the aerial platform on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of minicopters. Here, we present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. It enables tomographic reconstruction of gas plumes and a localization of gas sources.
Currently, UDT and BAM are cooperating to develop and validate an effective leakage detection method based on the UAV-REGAS platform. We present first results showing its performance under realistic conditions. Depending on the results, UDT may use this platform in their inspection practice in the near future.
Against the background of sustainable resource management and efficiency, wood-based materials are currently experiencing a revival and, among others, plywood, Laminated Veneer Lumber and glued laminated timber are becoming increasingly more important in the building sector. Even though these materials are so-called engineered products, the element wood is naturally grown with intrinsic variability in mechanical properties and requires professional handling on-site. Otherwise, load-bearing structures made of wood materials may entail certain risks. Critical situations can, in principle, be avoided by implementing a structural health monitoring system into components or structures made from wood material. The aim is to indicate accumulation of mechanical damage and to eliminate or at least significantly reduce the risk of unexpected failure. Toward this purpose, the failure behavior of several layered wood materials under quasi-static tension was investigated in laboratory-scale experiments by means of acoustic emission (AE) measurement. Based on spectral analysis and pattern recognition, two classes of AE signals are identified for each investigated lay-up that are characterized by either low or high frequency contents in the respective power spectra. AE activity and intensity of both signal classes are analyzed, striving for predictors appropriate for AE monitoring concepts.
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.
The testing of gas and humidity sensors is essential for the application of sensing devices. The testing determines properties of an investigated humidity sensor or gas sensor. In testing, the sensor property is the observed response of a sensing system to an input stimulus in comparison to a reference within a test procedure. This tutorial is structured into six chapters. The first chapter is about basics of hygrometry and gas sensing. It includes definitions, sensor classification, overview of application categories, as well as construction and functional principles. The chapter on requirements of deployed sensors focus on stability, response time, cross-sensitivity, electrical and mechanical requirements, and other. The sensing performance is characterised by determinations of parameters like accuracy, repeatability, detection limits (low and high), and response/recovery time. The description of testing signals on specified qualitative and quantitative characteristics, and the adequately estimated measurement uncertainty is the content of the analysis chapter. All chapters contain examples of testing procedures from testing/calibration labs for hygrometers and gas sensors which are essential for implementation into quality management systems.
This work presents the testing of a developed electronic nose for outdoor odour nuisance monitoring. The sensor system consists of a sampling system, a measuring cell equipped with commercially available low-cost gas sensor elements and a data acquisition system. The sensor testing showed that the developed prototype is able to distinguish, identify and partially quantify individual odorous substances like ethanol, ammonia and hydrogen sulphide. Ethanol and ammonia could even be detected below or near their odour threshold.
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
Simulating a Gas Source Localization Algorithm with Gas Dispersion Produced by Recorded Outdoor Wind
(2022)
This presentation reports the use of the first gas dispersion simulator capable of generating large wind fluctuation, to test a modification to a gas source localization algorithm and study how the change affects it. Gas source localization at an outdoor environment is a challenging task mainly due to the complexity of the spreading gas caused by the unpredictable nature of constantly changing wind. Therefore, a novel use of outdoor wind in developing a gas source localization system by simulation will be shared. To consider the characteristic of sudden but large and unpredictable changes in wind direction, recorded outdoor wind was used to simulate a realistic outdoor gas dispersion which has been done for the first time. With the use of this simulator, a modification to a mobile robot-based gas source localization algorithm was evaluated. Multiple simulations of the modified and the original algorithm were done to study the effect of the tested modification. The results showed that a small difference in the algorithm can greatly impact the results. From this study, we consider the use of simulation consisting of the necessary traits to evaluate outdoor gas source localization, has the potential to accelerate the development of a reliable localization system.
The determination of the measurement uncertainty of quantitative and qualitative results is an important quality management tool, for example to describe measuring equipment, procedures, measurement results and the quality of products. The lecture presents the process description for the determination of the measurement uncertainty according to GUM, the determination of uncertainties in qualitative measurement results and the application of the measurement uncertainty for conformity assessment.
Sensor systems are widely used in hydrogen technologies. The lecture presents requirements and properties of sensor systems and shows application examples of these technologies for safety and process control for hydrogen technologies. The focus is on gas sensors as well as sensors for pressure, temperature and gas flow. These systems are applied, among other things, for monitoring and control of operating conditions, indication of hazardous conditions and triggering of alarms.
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.
Sensor network deployment
(2018)
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.
The evaluation of measurement uncertainty is essential for testing and calibration laboratories. This paper provides a general process description for the determination of the measurement uncertainty of quantitative data and its application to the conformity assessment with examples, and how to deal with the uncertainty of qualitative (Boolean) results, e. g. when "Alarm" or "No alarm" occours in an instrument display.
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.
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.
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.
The presentation gives a brief overview of the main goals, the used methods and the first results of the PMARS OdourSense project. In cooperation with the Moulay Ismaïl University in Meknès (Morocco), a multi-gas sensor system for odour nuisance monitoring in outdoor air and a method for testing/calibration of such devices are being developed in this project.
Multi-functional coatings are a key requirement for surface engineering. General demands are adhesion and long-term stability under service conditions. The modification of surfaces by means of PVD-, ECD- or hybrid processes allows an add-on functionalization of surfaces by a huge diversity of materials with both lateral and vertical micro-/nano-designs. This fact is a prerequisite for micro- and sensor-systems in lab-on-chip and sensor-on-chip technology. Two layer-based sensor principles are presented, i.e. surface plasmon resonance enhanced spectroscopic ellipsometry (SPREE) for detection of hazardous gases and magneto-optical sensors on smart-coated fiber Bragg gratings (FBG) for structural health monitoring (SHM).
The interdependence of substrate features, coating properties, and layer design is discussed firstly for gas sensitivity and selectivity of SPREE-sensors and secondly for sensitivity and selectivity of magneto-strictive coatings to mechanical strain or external magnetic fields resulting in an optical displacement of the Bragg wavelength of FBG-sensors. Moreover, generic features such long-term stability, crucial process-related fabrication conditions, and effects of operational and environmental parameters are discussed with respect to the sensor performance. It has been shown that appropriate layer design and adapted selection of layer materials (SnOx/Au, Fe/Ni:SnOx/Au; Ni/NiFe-Cu-Cr) result in improved sensor parameters and may enable new sensor applications.
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.
Precise knowledge about the distribution of air pollutants is necessary to develop plausible occupational health measures. Combinatory systems, consisting of mobile robots and stationary sensors, can be effective solutions for the coverage of large measurement areas. However, further research is needed to fully understand their performance in comparison to traditional sensing strategies. Therefore, multiple sensor networks layouts will be set up in a simulation environment as well as in real industrial environments. Models for distribution mapping will be developed and evaluated to investigate the performance and opportunities of hybrid-mobility sensor networks for the task of distribution mapping.
In the present paper the development of a semi-automated device for long-term monitoring of gaseous ammonia is described. A sensor material was produced that changes its optical properties in the pres-ence of low concentrations of ammonia in air. The implementation into an electronic device enables precise, simple, economic and fast monitoring of low concentrations of harmful gases, like ammonia, and hence can help to improve the climate monitoring in livestock housing, barns or stables.
To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels
of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.
This work presents a first attempt to use ionic liquids as a new coating for planar coulometric sensors. These sensors are used for the measurement of trace humidity in various gases. Usually, the coating of the sensors is tetraphosphorus decaoxide and its hydrolysis products. Instead, a hygroscopic ionic liquid was used as sensor coating in this work. Generated frost point temperatures tf in the gas ranged from -80 °C to -30 °C, which is equivalent to vapour mole fractions xv from 0.5 μmol·mol-1 to 376 μmol·mol-1. In addition to the coulometric sensors, the generated humidity is determined by a precision dew point hygrometer as reference. First results show that it is possible to measure humidity withionic liquid based coulometric sensors.
Introduction of H2Safety@BAM
(2021)
This contribution for the Open Forum of the 8th HSN Symposium introduces the SensRef activities of the Competence Centre H2Safety@BAM.
In this paper, we introduce a nano aerial robot swarm for Indoor Air Quality (IAQ) monitoring applications such as occupational health and safety of (industrial) workplaces. The robotic swarm is composed of nano Unmanned Aerial Vehicles (UAVs), based on the Crazyflie 2.0 quadrocopter, and small lightweight Metal Oxide (MOX) gas sensors for measuring the Total Volatile Organic Compound (TVOC), which is a measure for IAQ. An indoor localization and positioning system is used to estimate the absolute 3D position of the swarm similar to GPS. A test scenario was built up to validate and optimize the swarm for the intended applications. Besides calibration of the IAQ sensors, we performed experiments to investigate the influence of the rotor downwash on the gas measurements at different altitudes and compared them with stationary measurements. Moreover, we did a first evaluation of the gas distribution mapping performance. Based on this novel IAQ monitoring concept, new algorithms in the field of Mobile Robot Olfaction (MRO) are planned to be developed exploiting the abilities of an aerial robotic swarm.
The lecture about gas sensor characterization and calibration contains the content of 3S: Sensitivity, selectivity and stability, sensor drift, aging and poisoning as well as gas mixing systems for laboratory and field calibration, measurement uncertainty for sensor calibration and traceable calibration of gas sensor systems.
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.
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.
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.
Fluorometric sensing is a versatile approach for trace analysis outside of the laboratory, requiring suitable sensor materials and their integration into sensing devices. The versatility of fluorophores as probes, especially in terms of the possibility to tailor their optical as well as their recognition properties by synthetic modifications in a wide range, renders them as superior active component for the preparation of optical sensor devices. Recent works at BAM in this field include, for example, the detection of nerve gas agents,illustrating impressively the aforementioned benefits of fluorophores in optical sensing applications.
In the interdisciplinary approach presented here, we target hazardous gases such as ammonia, benzene, and hydrogen sulfide, next to others, which pose a major threat to human health and environmental safety and for which the availability of a sensitive and reliable detection method is highly desirable.
The dyes presented follow a “turn-on” fluorescence schematic, which allows for the selective and sensitive detection of the respective gaseous analyte. The immobilization of the probe in polymeric matrices is then the next step toward the fabrication of a prototype device for molecular sensing. Further steps in the project include the assembly of instruments for test-atmosphere generation, the referencing of the sensor system, development and implementation of an optical setup, and the testing of the prototype device under laboratory conditions and in the field.
In this presentation, we give an overview over the recent developments on this topic in our groups, including fluorophore designs investigated for the detection of benzene, ammonia, and hydrogen sulfide as well as approaches for the design of the sensing device.
Fluorometric sensing is a versatile approach for trace analysis outside of the laboratory, requiring suitable sensor materials and their integration into sensing devices. The versatility of fluorophores as probes, especially in terms of the possibility to tailor their optical as well as their recognition properties by synthetic modifications in a wide range, renders them as superior active component for the preparation of optical sensing devices. Recent works at BAM in this field include, for example, the detection of nerve gas agents, illustrating impressively the aforementioned benefits of fluorophores in optical sensing applications.
In the interdisciplinary project presented here, we target hazardous gases such as ammonia, benzene, and hydrogen sulfide, next to others, which pose a major threat to human health and environmental safety and for which the availability of a sensitive and reliable detection method is highly desirable. The dyes presented follow a “turn-on” fluorescence schematic, which allows for the selective and sensitive detection of the respective gaseous analyte. The immobilization of the probe in polymeric matrices is then the next step toward the fabrication of a prototype device for molecular sensing. Further steps in the project include the assembly of instruments for test-atmosphere generation, the referencing of the sensor system, development and implementation of an optical setup, and the testing of the prototype device under laboratory conditions and in the field.
In this presentation, we give an overview over the recent developments on this topic in our groups. Highlights are hydrogen sulfide sensitive, BODIPY based transition metal complexes, which allow for a sensitive as well as selective detection of the toxic gas. In addition, we present a novel class of highly substituted BODIPY derivatives – pocket-BODIPYs – which are of a synthetically high versatility and can readily be modified to create pockets in the periphery of the molecule of defined geometries. This is illustrated on the successful encapsulation of benzene by a pocket-BODIPY derivative, confirmed by X-ray crystallographic analysis as well as by further spectroscopic and analytical methods.
Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring
(2021)
A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown.
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.
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.
Acoustic emission testing
(2018)
Typically, the near-eutectic Al-Si alloys consist of highly interconnected three-dimensional network of the eutectic Silicon (Si) and intermetallics embedded into Aluminium (Al) matrix. For further improvement of the mechanical properties of such alloys, often, one single ceramic reinforcement phase, e.g. silicon carbide (SiC) or aluminium oxide (Al2O3) in the form of fibres or particles is added. However, hybrid reinforcements (fibres and particles) can further improve wear resistance and fracture toughness, and additionally, reduce anisotropy of the material. The engineering of metal matrix composites (MMC) for specific application requirements benefits from a comprehensive knowledge of the failure behaviour. Therefore, damage evolution under compression was investigated on:
- pure near-eutectic AlSi12CuMgNi matrix alloy
- type I: matrix reinforced with random-planar oriented Al2O3 short fibres (15 vol.%)
- type II: matrix reinforced with random-planar oriented Al2O3 short fibres (7 vol.%) and additional SiC particles (15 vol.%)
The analysis of damage mechanism was carried out in two rather independent but complementary studies. First, selected sister samples of every material were exposed to quasi-static compression (traverse control). The compression tests were interrupted at different strain levels. Miniature cylinders with a diameter of 1mm were extracted from the pre-strained samples and investigated by synchrotron computed tomography (SX-µCT) with a spatial resolution of about 0.7 µm. For the pure matrix alloy, microcracks are confined to the intermetallic particles and to the eutectic Si, hence no damage was observed in the Aluminium. The composite type II revealed a more effective strain accumulation (less damage) than type I at low plastic strain (up to 5 %), but a more catastrophic damage development due to cracking of the SiC clusters at higher strain levels.
The second approach to study the damage initiation and accumulation in the materials subjected to compressive load was Acoustic Emission (AE) analysis. In this case the in-situ monitoring of the acoustic emission signal was performed during compression tests on specimens with dimension of several mm. For all three material types, AE activity set at 2% strain. Differences in AE behaviour of the three materials was proven based on AE hitrate, signal peak amplitudes as well as weighted peak frequencies (WPF). Future work focuses on combination of AE and SX-µCT aiming for more detailed knowledge on damage mechanism of metal matrix composites.
Acoustic Emission
(2018)
Das Phänomen Schallemission (eng. Acoustic Emission, AE) ist eine Begleiterscheinung bei aktiven Mechanismen und Prozessen wie sie in Werkstoffen auftreten, die einer Belastung durch Krafteinwirkung oder durch Änderung der Umgebungsbedingungen ausgesetzt sind. Plastische Verformung, Risswachstum, Korrosion oder der Übergang zu einer anderen Phase generieren u.a. transiente elastische Wellen, die sich im Werkstoff und an der Materialoberfläche ausbreiten. Auftretende Auslenkungen von wenigen hundert Pikometern sind mit piezo-elektrischen AE Sensoren detektierbar. Aufzeichnungsraten von bis zu 40 MHz ermöglichen dabei zeitliche Auflösungen von einigen Nano- bis Mikrosekunden. Die Dechiffrierung der detektierten Informationen ist für die Material- und Versagenscharakterisierung sowie für Anwendungen in der Struktur- oder Prozessüberwachung ein zentraler Forschungsschwerpunkt.
Nach einem kurzen Überblick zu den Grundlagen werden ausgewählte Ansätze der AE Methode vorgestellt und diskutiert:
1) AE Monitoring von Pipelines als Referenzmessung für die Entwicklung eines akustischen faseroptischen Monitorings (AGIFAMOR)
2) Schwellwertfreie AE Messung im 4-Punkt-Rohrbiegeprüfstand BAM_9.1 (AGIFAMOR)
3) Kombination von AE mit Röntgen-Mikrotomografie und die Anwendung der frequenzbasierten Mustererkennung ermöglichen eine Zuordnung von AE und Mechanismen (Ergebnisse aus dem SNSF Projekt Nr. 127'134, Baensch 2014, Diss. ETH No. 22372)
Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring.
A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used.
Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms.
For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing.
In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account.
Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model.