One of the key research directions to increase the capacity of new radio (NR) vehicle-to-everything (V2X) communication systems is extension of employed frequency bands from sub-6 GHz to millimeter wave (mmWave) range. To investigate different propagation effects between sub-6 GHz and mmWave bands in high-mobility scenarios, one needs to conduct channel measurements in both frequency bands. Using a suitable testbed setup to compare these two bands in a fair manner, we perform channel measurements at center frequencies of 2.55 GHz and 25.5 GHz, velocities of 50 km/h and 100 km/h, and at 126 different spatial positions. Furthermore, we conduct a comparative study of the multi-band propagation based on measurement results. We estimate the power delay profile (PDP) and the Doppler power spectral density (DSD) from a large set of measurements collected in a measurement campaign. Finally, we compare measured wireless channels at the two employed frequency bands in terms of root-mean-square (RMS) delay spread and RMS Doppler spread.

Analysis and modeling of wireless communication systems are dependent on the validity of the wide-sense stationarity uncorrelated scattering (WSSUS) assumption. However, in high-mobility scenarios, the WSSUS assumption is approximately fulfilled just over a short time period. This paper focuses on the stationarity evaluation of high-mobility multi-band channels. We evaluate the stationarity time, the time over which WSSUS is fulfilled approximately. The investigation is performed over real, measured high-mobility channels for two frequency bands, 2.55 and 25.5 GHz. Furthermore, we demonstrate the influence of the user velocity on the stationarity time. We show that the stationarity time decreases with increased relative velocity between the transmitter and the receiver. Furthermore, we show the similarity of the stationarity regions between sub-6 GHz and mmWave channels. Finally, we demonstrate that the sub-6 GHz channels are characterized by longer stationarity time.

Growing intelligent transportation systems demand a vehicular communication technology that can satisfy high requirements in terms of data rates, latency, reliability and number of connected devices. To evaluate the performance of such communication technology, real-world measurements are required for various channel conditions. Since vehicular measurement campaigns are expensive and time-consuming, a high-mobility environment poses enormous challenges for performance measurements. Using the existing technique of time-stretching the transmit signals, such experiments can be emulated through measurements at a single lower velocity by inducing effects caused by higher velocities. The existing time-stretching technique poses the problem of different channel estimation quality between the time-stretched and the original system. To ensure that the technique gives accurate results in practical systems, we adapt the pilot-based channel estimation scheme within the existing time-stretching technique. Furthermore, we evaluate the proposed channel estimation scheme through simulations and a high-speed vehicular channel measurement campaign at the center frequency of 2.55 GHz.

Condition monitoring software is crucial for companies from all industrial branches that take care of the high availability of their automation systems. However, as the automation systems increase in complexity to support numerous business needs, the complexity behind the condition monitoring software development increases as well. It demands a deep understanding of various domain-specific requirements, state-of-the-art architectural concepts, and implementation technologies by software engineers. This paper copes with this complexity by proposing a model-driven approach to support software engineers in designing and implementing condition monitoring software. In that context, the paper contributes with a domain-specific language for condition monitoring software development (DSL4CMSD) along with a code generator that produces a set of python-based microservices. Furthermore, the paper discusses condition monitoring domain-specific requirements and presents a design process for their implementation using the DSL4CMSD. Finally, we evaluate the applicability of our modeling approach on the industrial heat exchanger monitoring case study.

Next-generation intelligent transportation systems require a communication technology that can satisfy high demands in terms of latency and reliability. One of the promising communication technologies to satisfy such demands is the fifth-generation (5G) new radio (NR) for the vehicle-to-everything (V2X). To support the development of NR-V2X systems in the 5.9 GHz band, it is essential to perform channel measurements in high-mobility scenarios. High-mobility scenarios in the 5.9 GHz band have been well investigated through drive-by measurement campaigns. Although such measurements deliver real-world results, they do not allow for controlled experiments. To investigate propagation characteristics of the 5.9GHz band, we perform controllable and repeatable channel measurements at different velocities (25, 50, 100 and 125 km/h) in an urban environment. Furthermore, we compare measured wireless channels in terms of the normalized average local scattering function.

Multi-agent modeling is suitable to simulate complex interaction dynamics of microscopic urban road traffic. Valuable motion predictions can systematically be generated and exchanged among the participants (agents) to study and quantity benefits of advanced V2X-communication, for example. However, such predictions are inherently uncertain which needs to be considered for traffic safety. This work proposes a stochastic motion prediction and evaluation approach suitable for multi-agent-based simulation and control. Dynamic occupancy probability grid maps are constructed, and their interpretation clearly shows the uncertainty generated by unknown road user intentions or traffic interactions. By formulating joint occupancy probability maps, a quantification of near-accident risk becomes possible which seems to be a promising tool to examine safety aspects in “non-critical” traffic situations. The studies are based on published naturalistic driving measurement data, and both data-based as well as model-based predictions are discussed.

Next-generation mobile communication systems employ millimeter wave (mmWave) frequency bands with high bandwidths to enable high data rate transmissions. Further, the importance of high mobility scenarios, such as vehicular communication or high-speed train scenarios, is steadily increasing. To learn how wave propagation and scattering effects change from classical sub 6 GHz to mmWave frequencies, measurements in both bands have to be conducted. We perform wireless channel measurements at 2.55 GHz and 25.5 GHz center frequency at high mobility. To ensure a fair comparison between these two frequency bands, we perform repeatable measurements in a controlled environment. Our measurement methodology enables measurements at the same transmitter and receiver positions and velocities, but at different center frequencies. We compare measured wireless channels at the two employed frequency bands in terms of the delay-Doppler function.

Condition monitoring is a fundamental technology that enables predictive maintenance of automation systems. However, as automation systems increase in complexity, the development of condition monitoring software becomes a challenging task that requires extensive knowledge from multiple engineering disciplines. In this context, the identification and specification of condition monitoring software requirements play a key role. Neglecting these tasks often results in costly problems during later stages of systems development. Currently, means to support interdisciplinary requirements engineering within condition monitoring software development are missing. In particular, there is a need for a systematic process that supports modeling condition monitoring requirements. In this paper, we present our solution - a profile based on the extension of the SysML, which is commonly used to engineer requirements in automation systems. The profile allows specification of condition monitoring software requirements and thus enables a more domain-specific requirements engineering approach. We illustrate this approach on a heat exchanger condition monitoring system, explain the particular modeling steps, and present lessons learned.

The process of beer brewing is very complex as it has to fulfill strict demands on the product quality as well as on the availability and the performance of the plant. As a consequence, a condition monitoring of the beer brewing process and its visualization plays an important role such that all relevant deviations are detected as early as possible by the production manager. While a general process for condition monitoring already exists, there currently exists no approach to realize this process for the domain of beer brewing.Therefore, this paper presents a condition monitoring system for the beer brewing process developed in an industrial project. This condition monitoring system is based on Key Performance Indicators (KPIs) that support the production manager in evaluating the actual state of the production processes. A set of brewery-specific KPIs are determined and discussed in the paper. In addition, software architecture and visualization of the KPIs in a brewery-specific dashboard are presented. We evaluate our concept at various beer breweries and report about lessons that we have learned.

Condition monitoring enables companies from all industrial branches to maintain high availability of their automation systems. Thus, condition monitoring is a fundamental technology to maximize operational productivity by predicting potential fault occurrences that lead to unavailability of the automation systems. Today, performing condition monitoring is successfully achieved using custom-developed software solutions. However, the development of these software solutions is a challenging task as it requires extensive knowledge from multiple engineering disciplines. In this context, there is a high need for a systematic way to formalize this knowledge. Ontologies have had significant success in fulfilling this need by providing a formal model of domain concepts and their relations.In this paper, we present our condition monitoring ontology for automation systems and contribute with a conceptual model. This ontology incorporates ISO standards for condition monitoring and key performance indicators (KPIs). KPIs aggregate numerous sensor values in a few understandable numbers and, therefore, provide more efficient insight in condition of automation systems. To evaluate our work, we developed a condition monitoring knowledge-based system for a centrifugal separator based on the proposed ontology in an industrial project and report about lessons learned.

Communication between two or more participants is obtained through a non ideal transmission medium. Such medium is usually characterised by different types of distortions which affect transmitted signal. Many of them are not completely avoidable. In order to minimize their effect at the receiver end, adequate performance measure of distortion-to-signal influence is needed. As the one of the most reliable measures for evaluating total distortion on transmitted signal, from transmitter to receiver end, we consider a bit error rate. In this paper, we propose a low-cost bit error rate measuring system constructed as RTL-SDR based wireless Hardware-In-The-Loop system. The proposed measuring system is tested in indoor environments on BPSK modulated signals. Obtained results show high matching with theoretical expectations. Also, for the proposed system adequate graphical user interface is created in the Matlab.

High availability of automation systems is one of the main goals for the companies from all industrial branches. To achieve and maintain this high availability, the condition monitoring of the automation systems is an essential building block. However, as automation systems become increasingly equipped with numerous mechanical, electrical, and software components, creating a condition monitoring solution is becoming more and more challenging and requires knowledge from multiple engineering disciplines. Today, creating a condition monitoring solution is mostly based on the experience and preferences of the developers without a systematic and interdisciplinary method. Today, methods and tools supporting an interdisciplinary development exist. However, they do not fully consider condition monitoring relevant information. In addition, tools that increase software productivity and ease the adjustment of condition monitoring software are lacking. The main goal of this paper is to narrow the condition monitoring expertise gap by proposing convenient, systematic, and automated techniques to support the development of condition monitoring solutions from their design to their implementation. To achieve this goal, we propose an extension of the CONSENS systems engineering method to face issues caused in the design phase. By adopting a Model-Driven Development (MDD) approach, we propose a Domain-Specific Language (DSL) for condition monitoring that promotes increased understandability, and automation during the software implementation phase.