8.1 Sensorik, mess- und prüftechnische Verfahren
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In this study, nanoscopic particles of magnesium Fluoride (MgF2) and calcium fluoride (CaF2) also known as nano metal fluorides (NMFs), were evaluated for their potential to improve wood durability. Even though these fluorides are sparingly soluble, their synthesis in the form of nano-sized particles turns them into promising candidates for wood preservation. Their distinct property of low-water solubility is proposed to maintain long-lasting protection of treated wood by reducing the leaching of fluoride. Analytical methods were used to characterize the synthesized NMFs and their distribution in treated wood specimens. Transmission electron microscopy images showed that these fluoride particles are smaller than 10 nm. In nano metal fluoride (NMF) treated specimens, aggregates of these particles are uniformly distributed in the wood matrix as confirmed with scanning electron microscopy images and their corresponding energy-dispersive X-ray spectroscopy maps. The fluoride aggregates form a protective layer around the tracheid walls and block the bordered pits, thus reducing the possible flow path for water absorption into wood. This is reflected in the reduced swelling and increased hydrophobicity of wood treated with NMFs. The biocidal efficacy of NMFs was tested against brown-rot fungi (Coniophora puteanaand Rhodonia placenta), white-rot fungus (Trametes versicolor), and termites (Coptotermes formosanus). The fungal and termite tests were performed in accordance with the EN 113 (1996) and EN 117 (2012) standards, respectively. Prior to fungal tests, the NMF treated wood specimens were leached according to the EN 84 (1997)standard. Compared to untreated specimens, the NMF treated wood specimens have a higher resistance to decay caused by brown-rot fungi, white-rot fungus, and termites. Although all NMF treatments in wood reduce the mass loss caused by fungal decay, only the combined treatment of MgF2 and CaF2 has efficacy against both brown-rot fungi and white-rot fungus. Similarly, wood treated with the combined NMF formulation is the least susceptible to attack by C. formosanus.It is proposed that combining MgF2 and CaF2changes their overall solubility to promote the release of fluoride ions at the optimal concentration needed for biocidal efficacy against fungi and termites. In this thesis, it was proven that even after leaching, sufficient fluoride was present to protect NMF treated wood from fungal decay. This shows that NMFs are robust enough for above ground contact outdoor applications of wood, where permanent wetness cannot be avoided according to Use Class 3.2, as per the EN 335 (2013) standard. Also, they pose a low risk to human health and the environment because they are sparingly soluble. Since NMFs significantly reduce the decay of wood, the CO2 fixed in it will be retained for longer than in unpreserved wood. Overall, the novel results of this study show the potential of NMFs to increase the service life of building materials made from non-durable wood.
This paper describes a wireless mobile prototype able to perform optical measurements by means of a miniaturized spectrometer for low light analysis, e.g. fluorescent sensors.
Evaluations, calculations, calibration management and result display are performed by a computer or a standard tablet. The device was designed primarily to detect traces of oil in drinking or ground water and for the analyses of crude oils. However, it can also address a wide range of fluorescent sensors. The fast and user-friendly inspection of water quality or oil properties, as well as the adaptability and mobility, make the device attractive for a variety of users. Further application areas could be easily implemented by adapting the optics and the software (database, data processing and calibration plots, etc.).
This paper describes a wireless mobile prototype able to perform optical measurements by means of a miniatur-ized spectrometer for low light analysis, e.g. fluorescent sensors. Evaluations, calculations, calibration management and result display are performed by a computer or a standard tablet. The device was designed primarily to detect traces of oil in drinking or ground water and for the analyses of crude oils. However, it can also address a wide range of fluorescent sensors. The fast and user-friendly inspection of water quality or oil properties, as well as the adaptability and mobility, make the device attractive for a variety of users. Further application areas could be easily imple-mented by adapting the optics and the software (database, data processing and calibration plots, etc.)
Small unmanned aircraft are used increasingly as flying sensor platforms to perform tasks such as gas distribution mapping or environmental monitoring. Sometimes, these applications require knowledge of the ambient wind field.
In general, it would be interesting to avoid any additional hardware, both on ground and on the aircraft. Here, we present an approach to estimate the wind vector just by exploiting the state variables of the aircraft.
Small unmanned aircraft (UA) are used increasingly as flying sensor platforms. Amongst other things they are used for environmental monitoring, for example gas distribution mapping. Sometimes, these applications require knowledge of the ambient wind field, which can be measured by additional devices like anemometers. In general, it would be interesting to avoid such additional apparatuses. One way to achieve this is to estimate the wind vector, that is both the wind direction and speed, from the state variables of the UA.
An equation for wind vector estimation using a multirotor aircraft as a flying anemometer is shown. To compute the wind vector an estimate of the thrust of the aircraft is required, which is related to the rotational speed of the rotors. Hence, a sensing system for the rotational speed using phototransistors is presented.
The presence of wastewater and air pollution has become an important risk factor for citizens, not only in terms of problems related to health risks, but also because of its negative impact on the country's image. For this reason, malodorous emission monitoring and control techniques are in high demand in urban areas and industries. The aim of this work is first to build an electronic nose (e-nose) and a Voltammetric Electronic tongue (VE-tongue) in order to study their ability to discriminate between polluted and clean environmental samples. Secondly, Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS), and Solid Phase Micro Extraction-Gas Chromatography–Mass Spectrometry (SPME-GC-MS) are utilized to explain this discrimination by identifying specific compounds from these samples. Indeed, the e-nose, consisted of metal oxide semiconductor gas sensors, is used for the assessment of the studied odorous air and headspace samples from water and wastewater sites. Moreover, the VE-tongue, based on metal electrodes, is utilized to determine the patterns of the sensor array responses, which serve as fingerprints profiles of the analyzed liquid samples. Chemometric tools, such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vector Machines (SVMs) are operated for the processing of data from the e-nose and the VE-tongue. By using the both systems, the analyses of headspace and liquid samples from the seven sites allow better discrimination. To explain the cause of the obtained discrimination, TD-GC-MS and SPME-GC-MS analyses are well performed to identify compounds related sites. According to these outcomes, the proposed e-nose and VE-tongue are proved to be rapid and valuable tools for analysis of environmental polluted matrices.
Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
Die Optimierung und Validierung des digitalen Wasserstofftankstellenmanagements mit Sensortechnologien ist das übergeordnete Ziel des Teilprojekts Digitale Qualitätsinfrastruktur mit Sensortechnologien (QIST) am Use Case H2, im Rahmen der digitalen Qualitätsinfrastruktur (QI digital[1]). Hierfür sollen u.a. Sensornetzwerke mit digital-gestützten Auswertungsstrategien intelligent gestaltet werden (Stichwort KI bzw. digitaler Zwilling). Verschiedene, sich ergänzende Sensoren, Systemkomponenten und KI-Methoden stehen als Bausteine intelligenter Sensorsysteme zur Verfügung, mit dem Zweck, die physikalischen und chemischen Parameter an und in Anlagen umfassend und effizient zu überwachen sowie Fehlfunktionen zuverlässig zu detektieren und zu interpretieren, Abbildung 1.
Konkrete Arbeitsschritte sind, nach dem Aufbau der Versuchsplattform „Wasserstofftankstelle“, die Instrumentierung von Sensorik und deren digitale Integration in das Tankstellenmanagementsystem sowie die Validierung im Realbetrieb. Die somit erzielten Messergebisse sowie die Messunsicherheiten, Historien und Verfahren werden in digitaler Form aufbereitet, gespeichert und fortlaufend in die KI-basierte Datenauswertung einbezogen, inkl. der Anbindung an die metrologische Rückführungskette.
Ziele sind:
• Einführung eines neuen messtechnischen Ansatzes und eines digitalen Zwillings für den Anwendungsfall "Wasserstofftankstelle" auf Basis der H2-Sensorik sowie der Zustandsgrößen Druck und Temperatur
• Qualitätssicherung durch Applikationen zuverlässiger Gassensorik, Manometer und Thermometer sowie KI-Methoden zur Prozesskontrolle und Detektion von Fehlfunktionen
• Nutzung von digitalen Kalibrierscheinen (DCCs) insbesondere für die Messgröße Temperatur zur Realisierung der metrologischen Rückführung in einer digitalen Qualitätsinfrastruktur
The applicability of three methods developed by finite element analysis (FEM) and proposed in the literature are studied on steel S355. Instrumented indentation tests using spherical indenters of radius 200 and 500 μ m are performed in the macro range at depths of more than 6 μ m. The results of the selected methods are compared with the tensile test. To evaluate the partially strongly varying results, the uncertainties of the calculated strain hardening exponent, n, and yield stress, Y, are estimated. Recommendations for an appropriated procedure of the indentation test are given. The machine compliance and the determination of the zero point of Depth play an essential role. If the certain conditions are considered, the instrumented indentation Tests can be used, in particular for investigations of specimens with inhomogeneous elastoplasticity.
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.
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.
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.
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.
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 costefficient 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 paper, we detail the technical aspects of RASEM and introduce the mobile platforms used.