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- 8.1 Sensorik, mess- und prüftechnische Verfahren (71) (entfernen)
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.
Multi-sensory monitoring and ultrasound for quality assurance at underground sealing structures
(2023)
Within the safety concepts of underground disposal sites of nuclear waste, engineered barriers play an important role. As these sealing structures have high demands concerning integrity, we aim at advancing the available construction materials, monitoring, and inspection techniques within the project SealWasteSafe. A specifically developed alkali-activated material is compared to classical salt concrete. A comprehensive multi-sensory monitoring scheme is used at 150-340 l specimens to monitor setting and hardening of both materials. All sensors are demonstrated to resist the highly alkaline environments. Besides cabled and wireless temperature and humidity of the materials, strain variations using fibre optic sensors and acoustic emissions are recorded over periods of at least 28 days, partly for more than eight months. After hardening of the specimens, further nondestructive evaluations using ultrasonic echo and thermographic measurements are conducted.
Preliminary results proof the suitability of the tested sensors and clearly highlight differences between the tested materials. Particularly, the newly developed alkali-activated material shows lower acoustic emission activity indicating less cracking activity. Additionally, unique ultrasonic methods will enable better images of potential internal objects and cracks at in-situ sealing structures. A largescale ultrasonic system is optimised to reliably detect objects at a depth exceeding 9 m while still obtaining a good resolution. Modelling studies show the potential of further increasing the distance between individual transducer arrays. Additionally, a new ultrasonic borehole probe using phased arrays allowing for beam focussing is constructed and tested. Laboratory measurements at a halfcylindrical concrete specimen coincide well with the previous modelling. In total, the presented safe materials, detailed monitoring approaches and ultrasonic quality assurance methods will help to obtain safe sealing structures within salt as a host rock. The concepts can partly be transferred to sealing structures in alternative host rocks and will also be valuable for non-nuclear waste repositories.
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.
In many cases, science falls back on self-developed prototype systems, which are used and developed for the measurement and execution of the tasks. About 80 % of the development is based on the same hardware design, which is used in only one application scenario and then discarded. For the most part, there are also uncalibrated sensors, since it is costly to calibrate a complicated sensor measurement system or the entire measurement chain since access to the sensor systems is not always available. This paper describes a conceptual design to implement a versatile sensor system with the motivation to fuse the data recording and data reception, which can cover large areas with the help of LoRaWAN (Long Range Wide Area Network) technology. To overcome disadvantages of LoRaWAN, namely the slow data rates, the proposed sensor system can also cover smaller areas with the widespread WiFi technology. An enormous advantage over individual complete systems in the form of a prototype, is the rapid expansion, uncomplicated calibration of the individual sensors and the ecological relief.
A modular design is used, where individual stacks with sensors and peripherals can be added separately. The stacks are standalone low-power systems and can be calibrated, maintained, and replaced separately and do not require the entire measurement chain. The measured and sent values are stored locally on the main stack and sent to the data collector (gateway) and evaluated by means of the automated selection between WiFi and LoRaWAN.
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.
Remote sensing of hazardous substances is a key task that can be achieved with the help of remotely operated platforms equipped with specific sensors. A huge variety of methods and used vehicles have been developed for different purposes in recent years. The term smart dust refers to a science fiction novel and develop shortly after into a research proposal at UC Berkley funded by DARPA. Subsequently, the topic gained attraction but was overall considered as to complex for the technologies available at that time. In the launched passive smart dust project, we shift to a simple “chemical intelligent” passive sensor particle on the ground combined a read-out active sensor attached to an Unmanned Aerial Vehicle (UAV). The reactive particle surface can be preadjusted in the lab for exact desired properties regarding certain reactions to hazardous substances.
Moreover, the aimed interaction with the active sensor can be modified. Planed applications allow for different materials e.g., for short time measurement, being ecologically degradable, or weather stable for long time monitoring.
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 Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation.