8.1 Sensorik, mess- und prüftechnische Verfahren
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Paper des Monats
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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.
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.
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.
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.
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.
Simulating a Gas Source Localization Algorithm with Gas Dispersion Produced by Recorded Outdoor Wind
(2023)
This paper reports the use of the first gas dispersion simulator capable of introducing large wind fluctuations into simulations. The proposed simulator enables testing of a modification made to a gas source localization algorithm in a realistic scenario in order to study how the change affects it. Gas source localization in an outdoor environment is a challenging task mainly due to the complexity of the gas spread 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 is presented in this paper. To consider the characteristic of sudden but large and unpredictable changes in wind direction, we propose to use recorded outdoor wind to simulate a realistic outdoor gas dispersion which has been done for the first time to the best of our knowledge. With the use of this simulator, we have tested a modification to a mobile robot-based gas source localization algorithm. Multiple simulations of the modified and the original particle filter-based algorithm have been 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 show that 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.
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.
Wastewater contains harmful chemicals and heavy metals that are known to cause various environmental and health problems. Therefore, the water quality control using sensitive, simple, fast, accurate, and portable tools is of great importance. This study aimed to evaluate the pollution parameters of wastewaters collected at different treatment stages from a wastewater treatment plant (WWTP) that treats domestic and industrial wastewaters by using an electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) combined with chemometric techniques. Water and wastewater pollution parameters determination were performed using inductively coupled plasma optical emission spectrometry for the determination of cations, and anions by using ion chromatography. Chemometric techniques, such as Principal Component Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to process the E-nose and E-tongue datasets to describe the similarities between the samples. In addition, Partial Least Squares Regression (PLSR) model was constructed using electronic sensing data to simultaneously predict the concentration values of physicochemical parameters. The obtained correlation coefficient, for training and testing sets, is higher than 0.91 for the prediction of the concentration of all physicochemical parameters, except for iron (Fe) which remains 0.84. These results suggest that simple, portable, and inexpensive tools such as electronic nose and tongue are suitable for wastewater analysis.
Till now application of printed magnetoelectronics is hindered by lack of large area exchange coupled metallic multilayers required to produce printable magneto-sensory inks. Large-scale roll-to-roll (R2R) fabrication process is an attractive approach owing to its capabilities for high volume, high throughput, and large area manufacturing. Precise and high performance R2R sputtering technology is developed to fabricate large area giant magnetoresistive (GMR) thin-films stacks that contain 30 metallic bilayers prepared by continuous R2R sputtering of Co and Cu sequential on a hundred meters long polyethylene terephthalate (PET) web. The R2R sputtered Co/Cu multilayer on a 0.2 × 100 m2 PET web exhibits a GMR ratio of ≈40% achieving the largest area exchange coupled room temperature magneto-sensitive system demonstrated to date. The prepared GMR thin-film is converted to magnetosensitive ink that enables printing of magnetic sensors with high performance in a cost-efficient way, which promotes integration with printed electronics. An average GMR ratio of ≈18% is obtained for 370 printed magnetic sensors. The realized precise R2R sputtering approach can also be extended to a wide range of hybrid thin-film material systems opening up a path for new functional inks applied with printing technologies.
The finite volume method (FVM), like the finite element method (FEM), is a numerical method for determining an approximate solution for partial differential equations. The derivation of the two methods is based on very different considerations, as they have historically evolved from two distinct engineering disciplines, namely solid mechanics and fluid mechanics. This makes FVM difficult to learn for someone familiar with FEM. In this paper we want to show that a slight modification of the FEM procedure leads to an alternative derivation of the FVM. Both numerical methods are starting from the same strong formulation of the problem represented by differential equations, which are only satisfied by their exact solution. For an approximation of the exact solution, the strong formulation must be converted to a so-called weak form. From here on, the two numerical methods differ. By appropriate choice of the trial function and the test function, we can obtain different numerical methods for solving the weak formulation of the problem. While typically in FEM the basis functions of the trial function and test function are identical, in FVM they are chosen differently. In this paper, we show which trial and test function must be chosen to derive the FVM alternatively: The trial function of the FVM is a “shifted” trial function of the FEM, where the nodal points are now located in the middle of an integration interval rather than at the ends. Moreover, the basis functions of the test function are no longer the same as those of the trial function as in the FEM, but are shown to be a constant equal to 1. This is demonstrated by the example of a 1D Poisson equation.
The mechanical and geometrical properties of impact targets greatly influence the outcome of a drop test. The International Agreement concerning the International Carriage of Dangerous Goods by Road (ADR) as well as ISO 2248 describe the characteristics of impact targets for drop tests of dangerous goods packagings.
According to these regulations, the impact target’s surface needs to be unyielding, under testing conditions non-deformable, flat, and integral with a mass at least 50 times that of the heaviest packaging to be tested. The problem is that many production facilities, especially manufacturers of corrugated fibreboard boxes, do not have their own testing device with the required 50 times mass ratio of the impact target for a regulation compliant drop test during series production. Furthermore, at UN level it is considered necessary to revise these requirements.
In the present paper, the impact target requirements are examined in detail and compared with those in other technical areas (e.g., impact target for container for the transport of radioactive materials). A research method is being developed to investigate the dependency between the mass ratio of the packaging and the target as well as the damage resistance of a drop tested package in relation to specific design characteristics. The results are of high relevance for industry purposes and intended to ensure a uniform level of safety assessment for the mechanical testing of dangerous goods packagings.
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.
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.
Monitoring of repositories for radioactive waste requires techniques which can be applied long-term under harsh conditions. In this work, the reliability and suitability of materials and a capacitive sensor for measuring relative humidity are investigated, which are to be embedded in the special concrete components for the closure structures of underground repository. Preliminary tests with accelerated aging of the materials used, validation of the sensors under difficult conditions, investigations of the surfaces after aging by pH 14 solution, defined exposure of the sensors in a climatic chamber and the microscope images are discussed. The results will be used for further development and optimization of the RFID based sensor systems which can be applied to monitor the condition of different building structures without cabling.
This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize a 5 cm wide chemical trail, a detection criterion was developed that uses only relative changes in the transient phase of the sensor response. The reduction in signal strength dependence improves the robustness of its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot can correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future.
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.
This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize the chemical trail, a novel detection criterion was developed that uses only relative changes in the transient phase of the sensor response, making it more robust in its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot is able to correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future.
Poor air quality, particularly in urban areas, causes various diseases and degrades living standards. Air Quality could be affected by emissions of odor, Volatile Organic Compounds (VOCs), and other gases. Therefore, assessment and monitoring of odorous air quality using sensitive, simple, rapid, accurate and portable tools is very important for public health. This study aimed to characterize odor emissions to detect malfunctions in facilities and to prevent air pollution and olfactory nuisance in the environment. A gas chromatographic method, in conjunction with sensorial analysis were performed for odorous air samples analysis collected from neighborhood of Meknes city (Morocco). Advanced multivariate statistical approaches, such as Principal Components Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to describe samples similarities. The electronic nose (e-nose) data processing exhibits a satisfactory discrimination between the odorous air samples. Twenty-four VOCs with known molecular formulas were identified with Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS). A validated Partial Least Square (PLS) model foresees good calibration between e-nose measurement and TD-GCMS analysis. The finding indicates that TD-GC–MS approach in conjunction with e-nose unit could be suitable tool for environmental measurement-based odor emissions.
Bending beams and slabs are typical examples for structural elements used for reinforced concrete structures such as bridge girders, T-beams and bridge decks. Their strength related failure modes at maximum loading can be divided into bending and shear failure. The failure of beams loaded in bending can occur with or without indication. Therefore, conventional design concepts aim on failure modes with sufficient indication (e.g. large deflections or cracks), as it occurs in the case of secondary flexural compression failure. These indicating factors can also be used for Structural Health Monitoring (SHM) of civil infrastructure systems (e.g. bridges) to identify structural changes. In this context, non-destructive testing (NDT) methods offer different techniques for measuring deflections or crack formation and opening. However, profound knowledge on the determining failure modes of bending beams and their detection by NDT methods is required for the reliable application of SHM. Different NDT methods have been used in this study for analysing the load-bearing behaviour of a reinforced concrete beam in bending. The different measuring techniques are briefly described and their applicability is discussed by means of experimental results. For this purpose, the load-bearing behaviour of a reinforced concrete beam having a span of 2.75 m was investigated in a four-point bending flexural test at laboratory scale. The focus is on the characterization of determining failure modes by optical NDT and the comparison with classical measuring techniques (e.g. deformation measurements by displacement transducers). The bending beam was equipped with two single-mode (SM) sensor fibres. One fibre served as Distributed Optical Fibre Sensor (DOFS), whereas the other fibre contained Fibre Bragg Grating (FBG) sensors. In addition, optical deformation measurements using Digital Image Correlation (DIC) and Stereophotogrammetry (SP) were conducted.
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.
Wood-based composites hold the promise of sustainable construction. Understanding the influence on wood cellular microstructure in the macroscopic mechanical behavior is key for engineering highperformance composites. In this work, we report a novel Individual Cell Tracking (ICT) approach for in-situ quantification of nanometer-scale deformations of individual wood cells during mechanical loading of macroscopic millimeter-scale wood samples. Softwood samples containing > 104 cells were subjected to controlled radial tensile and longitudinal compressive load in a synchrotron radiation micro-computed tomography (SRμCT) setup. Tracheid and wood ray cells were automatically segmented, and their geometric variations were tracked during load. Finally, interactions between microstructure deformations (lumen geometry, cell wall thickness), cellular arrangement (annual growth rings, anisotropy, wood ray presence) with the macroscopic deformation response were investigated. The results provide cellular insight into macroscopic relations, such as anisotropic Poisson effects, and allow direct observation of previously suspected wood ray reinforcing effects.
The method is also appropriate for investigation of non-linear deformation effects, such as buckling and deformation recovery after failure, and gives insight into less studied aspects, such as changes in lumen diameter and cell wall thickness during uniaxial load. ICT provides an experimental tool for direct validation of hierarchical mechanical models on real biological composites.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localise damage was examined in. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localisation responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed in. In this attempt, the systematic influences such as residual stress are kept as low as possible.
Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment’s characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network’s quality will be investigated.
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.
One challenge associated with navigating a nano aerial robot swarm indoors in unstructured environments is, i.a., the limited air space. To avoid an over-regulation of the available indoor air space (e.g., prohibit copters to fly above each other), a safety region around each copter must be defined. In this paper, we investigate the impact of the downwash of a nano-drone on the stability of another nearby nano-drone. In the experiments, we found out that this downwash has a negative influence of a second nano-drone in a distance of around 1 m vertically and 0.2 m horizontally. Based on the obtained data, we developed a model describing the above-mentioned safety region to enable a safe operation of the swarm in these environments with fewer constraints.
Imperceptible magnetic sensor matrix system integrated with organic driver and amplifier circuits
(2020)
Artificial electronic skins (e-skins) comprise an integrated matrix of flexible devices arranged on a soft, reconfigurable surface. These sensors must perceive physical interaction spaces between external objects and robots or humans. Among various types of sensors, flexible magnetic sensors and the matrix configuration are preferable for such position sensing. However, sensor matrices must efficiently map the magnetic field with real-time encoding of the positions and motions of magnetic objects. This paper reports an ultrathin magnetic sensor matrix system comprising a 2 × 4 array of magnetoresistance sensors, a bootstrap organic shift register driving the sensor matrix, and organic signal amplifiers integrated within a single imperceptible platform. The system demonstrates high magnetic sensitivity owing to the use of organic amplifiers. Moreover, the shift register enabled real-time mapping of 2D magnetic field distribution.
In this communication a novel concept for pH sensing is introduced which is specifically adapted to monitor carbonation induced corrosion in concrete structures. The method is based on a ratiometric measurement principle, exploiting the pH sensitive colour switching of thymol blue in the basic pH regime and the emissive properties of two different (Zn)CdSe/ZnS core shell quantum dots. The transition point of thymol blue in a Hydrogel D4 matrix was determined to be at around pH 11.6, which fits ideally to the intended application. Next to the fundamental spectroscopic characterization of the ratiometric response, a new design for a sensor head, suitable for the incorporation into concrete matrices is presented. Toward this, a manufacturing process was developed which includes the preparation of a double layer of polymers containing either thymol blue or a quantum dot mixture inside a porous ceramic tube. Results of a proof-of-priciple performance test of the sensor head in solutions of different pH and in cement specimens are presented, with encouraging results paving the way for future field tests in concrete.
The vulnerability of low-quality concrete of some bridges to ageing and deterioration while they are exposed to environmental conditions during their service life, influence of growing population and climate changes, is a well-known effect and requires survey. For survey, the development of a low cost, cost-efficient sensor network to be embedded for continuous or attached to the surface for periodic monitoring is essential.
Changing ground water conditions, flood or even leakage in fresh or disposal of waste water can lead to in-creased moisture content in building materials and structural elements as walls or floors. Moisture allows the transport of other adverse chemicals that might reduce the durability of structures. The paper presents a feasibility study about the capability of microwave modules as bluetooth® or ZigBee with frequency range at 2,4 GHz to create advanced Wireless Sensor Networks (WSN) for estimation and visualization of moisture distribution in infrastructure assets. It was found that the value of the received signal strength indication (RSSI) depends on the moisture content and can be the basis for the moisture distribution inside a massive structure.
The microwave modules may be used to compose networks outside and inside of building materials or structural elements. The higher the number of connections between BLE modules, the more precise and the more distant information can be obtained from the network. Mesh networking was applied to increase the quantitative information.
The current establishment of stretchable electronics to form a seamless link between soft or even living materials and the digital world is at the forefront of multidisciplinary research efforts, bridging physics, engineering and materials science. Magnetic functionalities can provide a sense of displacement, orientation or proximity to this novel formulation of electronics. This work reviews the recent development of stretchable magnetic field sensorics relying on the combination of metallic thin films revealing a giant magnetoresistance effect with elastomeric materials. Stretchability of the magnetic nanomembranes is achieved by specific morphologic features (e.g. wrinkles or microcracks), which accommodate the applied tensile deformation while maintaining the electrical and magnetic integrity of the sensor device. The entire development, from the demonstration of the world’s first elastically stretchable magnetic sensor to the realization of a technology platform for robust, ready-to-use elastic magnetosensorics is described. Soft giant magnetoresistive elements exhibiting the same sensing performance as on conventional rigid supports, but with fully strain invariant properties up to 270% stretching have been demonstrated. With their unique mechanical properties, these sensor elements readily conform to ubiquitous objects of arbitrary shapes including the human skin. Stretchable magnetoelectronic sensors can equip soft and epidermal electronic systems with navigation, orientation, motion tracking and touchless control capabilities. A variety of novel technologies, like electronic skins, smart textiles, soft robotics and actuators, active medical implants and soft consumer electronics will benefit from these new magnetic functionalities.
In civil engineering, many structures are made of reinforced concrete. Most Degradation processes relevant to this material, e.g., corrosion, are related to an increased level of material moisture.
Therefore, moisture monitoring in reinforced concrete is regarded as a crucial method for structural health monitoring. In this study, passive radio frequency identification (RFID)-based sensors are embedded into the concrete. They are well suited for long-term operation over decades and are well protected against harsh environmental conditions. The energy supply and the data transfer of the humidity sensors are provided by RFID. The sensor casing materials are optimised to withstand the high alkaline environment in concrete, having pH values of more than 12. Membrane materials are also investigated to identify materials capable of enabling water vapour transport from the porous cement matrix to the embedded humidity sensor. By measuring the corresponding relative humidity with embedded passive RFID-based sensors, the cement hydration is monitored for 170 days.
Moreover, long-term moisture monitoring is performed for more than 1000 days. The Experiments show that embedded passive RFID-based sensors are highly suitable for long-term structural health monitoring in civil engineering.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or hardly accessible areas. A challenging task, however, is the generation of threedimensional distribution maps from these gas measurements.
Suitable reconstruction algorithms can be adapted, for instance, from the field of computed tomography (CT), but both their performance and strategies for selecting optimal measuring poses must be evaluated. For this purpose simulations are used, since, in contrast to field tests, they allow repeatable conditions. Although several simulation tools exist, they lack realistic models of remote gas sensors. Recently, we introduced a model for a Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor taking into account the conical shape of its laser beam. However, the novel model has not yet been validated with experiments. In this paper, we compare our model with a real sensor device and show that the assumptions made hold.
This paper describes the development of a semi-automatic gas measurement device presenting potentially a broad range of applications, noteworthy in the agricultural sector. Non-reversible fluorescent molecular sensors were designed and syn-thesized. Upon, integration into a hydrogel matrix with an optimal ratio of co-solvents, the sensors reacting selectively to ammonia were illuminated by excitation light to produce a concentration-correlated fluorescence emission. An automated mechanical-elec-trical device initiates a given gas mixture and thus simulates con-centrations similar to a threshold value. The aim of this project is to develop a sensor or a low-cost method which can monitor low concentrations of harmful gases and aid in their elimination or regulation in livestock housing, barns or stables.
In this paper, we present and validate the concept of an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor with a 3-axis aerial stabilization gimbal for aiming at a versatile octocopter. While the TDLAS sensor provides integral gas concentration measurements, it does not measure the distance traveled by the laser diode’s beam nor the distribution of gas along the optical path. Thus, we complement the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from a set of integral concentration measurements. To allow for a fundamental ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present results showing its performance characteristics and 2D plume reconstruction capabilities under realistic conditions. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO).
Within the presented research project, experimental and numerical investigations were performed to develop a thin-shelled, modular, mobile element system made of a micro-reinforced ultra-high-performance ductile concrete (DUCON®). Material parameters were experimentally determined to adapt the material model within the numerical analysis applying the Drucker-Prager relationship. Afterwards, for validation of the numerical models, quasi-static and high-velocity impact tests were performed on plate-like structures. Finally, a suitable geometry of transportable barrier elements will be designed, which provides a maximum of resistance against impact by a minimum of weight and a maximum of mobility.
In this paper, we introduce a nano aerial robot swarm for indoor air quality monitoring applications such as occupational health and safety of (industrial) workplaces. The concept combines a robotic swarm composing of nano Unmanned Aerial Vehicles (nano UAVs), based on the Crazyflie 2.0 quadrocopter, and small lightweight metal oxide gas sensors for measuring the Total Volatile Organic Compound (TVOC) in ppb and estimating the eCO2 (equivalent calculated carbon-dioxide) concentration in ppm. TVOC is a measure for the indoor air quality. An indoor localization and positioning system will be used to estimate the absolute 3D position of the swarm like GPS. Based on this novel indoor air quality monitoring concept, the development and validation of new algorithms in the field of Mobile Robot Olfaction (MRO) are planned, namely gas source localization and gas distribution mapping. A test scenario will be built up to validate and optimize the gas-sensitive nano aerial robot swarm for the intended applications.
The Measurement- and Model-based Structural Analysis (MeMoS) integrates a finite element model into least squares adjustment and thus allows to evaluate a mechanical model and measurements in a combined analysis. To examine the capability to detect and localise damage using this integrated analysis MeMoS, a small-scale truss bridge made of aluminium profiles is built as a test specimen for this purpose.
In mobile robot applications, some sensors such as open-path gas detectors or laser rangefinders need to be aimed at specific targets in order to get the desired measurements. To do this in a fast and elegant manner, we present a spherical parallel manipulator with three degrees of freedom. Compared to typical serial manipulators, it offers superior dynamics and structural stiffness, which are important parameters for this type of task. We present the mechanical design and derive kinematic equations both to compute set-points for the desired orientation and to estimate the current state of the system. A PID controller is used to generate control signals.
Compressed natural gas (CNG) is a widely used automotive fuel in a variety of countries. In case of a vehicle fire where the safety device also malfunctions, a failure of the CNG automotive cylinder could occur. Such a cylinder failure is associated with severe hazards for the surrounding environment. Firstly, a comprehensive analysis is given below, summarizing various accidents involving CNG automotive cylinders and their consequences. In an extensive experimental program, 21 CNG automotive cylinders with no safety device were tested. Of the 21, burst tests were carried out on 5 Type III and 5 Type IV cylinders. Furthermore, fire tests with 8 Type III and 3 Type IV cylinders were conducted. Apart from cylinder pressure, inner temperature and cylinder mantle temperature, the periphery consequences, such as nearfield blast pressure and fragmentation are documented. The maximum measured overpressure due to a Type III cylinder failure was p = 0.41 bar. Each traceable fragment was georeferenced. All-in-all, fragment throw distances of d > 300 m could be observed. As one key result, it can be stated that the tested Type IV CNG cylinders showed less critical failure behavior then the Type III cylinders under fire impingement.
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.)
In this paper, we present an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS) combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present first results showing the 2D plume reconstruction capabilities of the system under realistic conditions.
Damage mechanisms analysis of reinforced concrete beams in bending using non-destructive testing
(2019)
Beams that are loaded in transverse direction are one of the main structural elements used in reinforced concrete structures. Bending beams are used, for example, in bridge girders or joists. The strength related failure modes at maximum loading can be divided into bending and shear failure that could occur with or without indication. Conventional design concepts used in practice for designing structural elements subjected to bending aim at failure modes with sufficient indication (e.g. large deflections or cracks), as it occurs in the case of secondary flexure-compression-failure. These indicators can also be used to identify structural changes of civil infrastructure systems (e.g. bridges) using non-destructive testing methods for Structural Health Monitoring (SHM). However, profound knowledge on the determining failure modes of bending beams is required for the reliable application of structural health monitoring. Therefore, resulting effects of different thermal and mechanical loads on the response of a structure are investigated by means of a bridge demonstrator. The results of this investigation are used for developing a monitoring system. Different non-destructive testing (NDT) methods have been used for analyzing the deformation behavior of a reinforced concrete beam in bending. The suitability of the different measuring techniques will be discussed by means of the obtained experimental results. For this purpose, the deformation behavior of a reinforced concrete beam having a span of 2.75 m was investigated in a four-point bending flexural test at laboratory scale. The focus is on the characterization of determining failure modes by the NDT methods and the comparison with classical measuring techniques (e.g. deformation measurements by displacement transducers). The bending tests have been accompanied by Acoustic Emission analysis (AE), optical deformation measurements using Stereophotogrammetry (SP) and Digital Image Correlation (DIC). The conducted tests have shown that the three-dimensional detection of deformations and resulting strains with optical techniques is beneficial for analyzing the damage mechanism of loaded structures and related crack formation and propagation. Optical techniques can be supplemented by acoustic emission testing which gives detailed information on crack formation and progressing damage. The combination of both techniques offers the application as efficient monitoring system for larger structures.
In case of a vehicle fire, an installed LPG (liquefied petroleum gas) tank with a malfunctioning safety device poses severe hazards. To investigate the consequences in case of tank failure, we conducted 16 tests with toroidal shaped LPG vehicle tanks. Three tanks were used for a Hydraulic Burst Test under standard conditions. Another three tanks were equipped with a statutory safety device and were subjected to a gasoline pool fire. The safety device prevented tank failure, as intended. To generate a statistically valid dataset on tank failure, ten tanks without safety devices were exposed to a gasoline pool fire. Five tanks were filled to a level of 20 %; the re-maining five were filled to a level of 100 %. In order to gain information on the heating process, three tem-perature readings at the tank surface, and three nearby flame temperatures were recorded. At distances of l = (7; 9; 11) m to the tank, the overpressure of the shock wave induced by the tank failure and the unsteady tem-peratures were measured. All ten tanks failed within a time of t < 5 min in a BLEVE (boiling liquid expanding vapor explosion). Seven of these resulted directly in a catastrophic failure. The other three resulted in partial failure followed by catastrophic failure. A near field overpressure at a distance of l = 7 m of up to p = 0.27 bar was measured. All ten tests showed massive fragmentation of the tank mantle. In total, 50 fragments were found. These 50 fragments make-up 88.6 % of the original tank mass. Each fragment was georeferenced and weighed. Fragment throwing distances of l > 250 m occurred. For the tanks with a fill level of 20 %, the average number of fragments was twice as high as it was for the tanks that were filled completely.
Especially trace amounts of water vapour in gases can be reliably determined by coulometric trace humidity sensors. The principle of these sensors is based on water vapour absorption in a hygroscopic layer and its subsequent electrolytic decomposition. The calibration of sensors was performed in the humidity range, expressed as frost point temperature, from −30°C to −80°C . This range is equivalent to volume fractions smaller than 376 µL·L−1. Generated humidity was measured with coulometric sensors and a chilled dew point hygrometer that was used as reference. An empirical non-linear function was found between sensor signal and measured reference humidity. This function consists of two parameters with a measurement uncertainty. Both calibration parameters were checked by means of one-way analysis of variance. It showed that gas specific function can be used for humidity measurement in nitrogen, hydrogen, dinitrogen monoxide, compressed and synthetic air. It is possible to determine trace humidity in all tested gases with an expanded uncertainty less than 2.1 K (coverage factor k=2 ) regarding frost point temperature.
Trace humidity was measured by using miniaturized planar coulometric sensors in technical gases such as hydrogen, nitrogen, helium, nitrous oxide, and synthetic air. Frost point temperatures tf in the gases ranged from −60 °C to −30 °C, which is equivalent to a vapour mole fraction xv from 10 μmol mol−1 to 376 μmol mol−1. In addition, the generated humidity was determined by using a precision dew point hygrometer as reference. Nonlinear calibration functions were calculated that correlated electric current (sensor signal) and reference humidity. Parameters of functions were tested with one-way analysis of variances (ANOVA) to prove if all used sensors had a similar behavior in the same gas during experiments. Results of ANOVA confirmed that averaged functions can be applied for trace humidity measurement in nitrogen, helium, nitrous oxide, and synthetic air. The calculated functions were negligibly different for nitrogen, helium, and synthetic air. In humidified nitrous oxide, a minor change of parameters was observed due to lower electrical currents. In total contrast to that, the measured sensor signals were significantly higher in humidified hydrogen and each sensor required its own calibration function. The reason was a recombination effect that favoured multiple measurements of water molecules. Nevertheless, it was possible to measure continuously trace humidity in all tested gases by using coulometric sensors with an expanded uncertainty below 2 K (k = 2).
This work reported the investigation on the effect of relaxation to the permittivity (epsilon' r ) characteristic of Mg1-x Zn x TiO3 ceramics for x = 0 – 0.5 (MZT0 – MZT0.5) measured from 1 Hz to 330 MHz. Within that frequency range, the relaxation effect that consists of the space charge (SC) and the dipolar (D) polarization mechanisms were identified. The contribution of the D relaxation in MZT0 – MZT0.2 systems extents overall from about 100 Hz to 330 MHz, while that in MZT0.3 – MZT0.5 systems is from 50 kHz to 330 MHz. The remaining frequencies, i.e. from 1 to 90 Hz for MZT0 – MZT0.2 and from 1 Hz to 50 kHz for MZT0.3 – MZT0.5, are attributed to the SC relaxation. The D polarization mechanism provides constant epsilon' r values which vary from (15.4 – 17.0) ± 0.3 throughout the samples. Contribution of the SC polarization mechanism to the characteristic is supported by the simultaneous presence of different content and level of resistivity of the secondary phase of (Mg1-αZnα)2TiO4 in MZT0 – MZT0.2 systems and of (Zn1-αMgα)2TiO4 in MZT0.3 – MZT0.5, along with the presence of the main Mg1-x Zn x TiO3 phase, as a result of the variation of zinc content in the systems.