Analytische Chemie
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- 8.1 Sensorik, mess- und prüftechnische Verfahren (56) (entfernen)
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.)
Der Beitrag präsentiert die erarbeiteten technischen Richtlinien zur Anwendung und Prüfung von Multigassensoren innerhalb der Richtlinienreihe VDI/VDE 3518 sowie in diesem Zusammenhang die Möglichkeiten der Sensorprüfung im akkreditierten Prüflabor des Fachbereichs 8.1 der BAM. Im Speziellen wird das in Kürze veröffentlichte Blatt 3 der Richtlinienreihe vorgestellt, das Bezug auf Multigassensoren für geruchsbezogene Messungen mit elektronischen Nasen nimmt.
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.
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.
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.
The best-known discretization methods for solving engineering problems formulated as partial differential equations are finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). While the finite volume method is used in fluid mechanics, the finite element method is predominant in solid state mechanics. At first glance, FVM and FEM are two highly specialized methods. However, both methods can solve problems of both solid mechanics and fluid mechanics well. Since experimental mechanics deals not only with solid state physics but also with fluid mechanics problems, we want to understand FVM in the sense of FEM in this work. In the long term, we want to use the variational calculus to unify many important numerical methods in engineering science into a common framework. In this way, we expect that experiences can be better exchanged between different engineering sciences and thus innovations in the field of experimental mechanics can be advanced. But in this work, we limit ourselves to the understanding of the FVM with the help of the variational calculus already known in FEM. We use a simple 1D Poisson equation to clarify the point. First, we briefly summarize the FVM and FEM. Then we will deal with the actual topic of this paper, as we establish the FEM and the FVM on a common basis by variation formulation. It is shown here that the FVM can be understood in terms of the finite element method with the so-called Galerkin-Petrov approach.
This work presents the testing of a developed elec¬tronic 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.
Temperature characteristics of a piezoresistive accelerometer for high impact shock application
(2018)
This study presents the characterization of a piezoresistive accelerometer damped with silicon oil for the application in drop tests carried out at BAM. Experiments were performed with the Hopkinson Bar method in close correlation to the real-world application conditions. The results point out certain limitations regarding the temperature influence and the frequency response. Additional experiments were performed with a gas damped type of piezoresistive accelerometer, which has superior specifications, particularly for low temperatures. The results allow for a comparison.
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.