Developing Industrial CPS: A Multi-Disciplinary Challenge.

Industrial Cyber-Physical System (CPS) is an emerging approach towards value creation in modern industrial production. The development and implementation of industrial CPS in real-life production are rewarding yet challenging. This paper aims to present a concept to develop, commercialize, operate, and maintain industrial CPS which can motivate the advance of the research and the industrial practice of industrial CPS in the future. We start with defining our understanding of an industrial CPS, specifying the components and key technological aspects of the industrial CPS, as well as explaining the alignment with existing work such as Industrie 4.0 concepts, followed by several use cases of industrial CPS in practice. The roles of each component and key technological aspect are described and the differences between traditional industrial systems and industrial CPS are elaborated. The multidisciplinary nature of industrial CPS leads to challenges when developing such systems, and we present a detailed description of several major sub-challenges that are key to the long-term sustainability of industrial CPS design. Since the research of industrial CPS is still emerging, we also discuss existing approaches and novel solutions to overcome these sub-challenges. These insights will help researchers and industrial practitioners to develop and commercialize industrial CPS.

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions.

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.

Comparison of different model approaches for a hygiene early warning system at the lower Ruhr River, Germany.

The lower Ruhr River is located in a densely populated and industrialized area in Northrhine-Westphalia (NRW) in western Germany. Due to upgrades of sanitary infrastructure, such as wastewater treatment plants (WWTPs) and combined sewer overflows (CSOs), and a decline of industrial production, water quality of Ruhr River has been constantly increasing over the past decades. One effect is a growing attractiveness of the Ruhr for bathing and water sports. In order to enable future bathing in the lower Ruhr, this study investigates methods for predicting the permissibility of bathing, according to the microbial water quality regulations of the Bathing Water Ordinance of Northrhine-Westphalia (NRW-BWO). On basis of the European Commission Bathing Water Directive, the NRW-BWO defines methods for the assessment of bathing water quality on basis of bacterial threshold concentrations of Escherichia coli (E. coli) and intestinal enterococci (Int. Ent.). Furthermore, if the bathing water is subject to short-term pollution, the NRW-BWO requires the installation of an early warning system to prevent bathers' exposure. Laboratory detections of both bacteria species from water samples are not suitable to be used in an early warning system. Online measurement devices for bacteria showed to be not sensitive and accurate enough to reliably indicate an exceedance of the threshold values. Thus, the application of a prediction model is appropriate. In total, four different modeling approaches were developed and compared to provide short-term predictions of bacterial concentrations: (i) statistical modeling based on linear correlations between hydro-chemical parameters, such as ammonia and turbidity, and bacteria, (ii) modeling based on artificial neural networks (ANNs), which consider non-linear correlations between hydro-chemical and climate parameters and bacteria concentrations, (iii) a balance model, which considers all in- and outflows, both in terms of water quality and quantity, along a stretch of the lower Ruhr River, and (iv) binary modeling based on precipitation rates, as rainfall is assumed to trigger high bacteria loads in the river. It could be shown that ANNs allow the most accurate prediction of bacterial concentrations in the lower Ruhr River. However, the model performance varies among different stretches along the Ruhr River. This indicates that local conditions, e.g. distance to next upstream WWTP or CSO, are essential and need to be further investigated. The binary model which considered rainfall effects also provided acceptable short-term predictions. For example, at all potential bathing spots, after two days following substantial precipitation amounts, bathing would have been allowed. The balance model showed the weakest results, which is mainly due to data gaps, as time series of bacterial loads from tributaries, WWTPs and CSOs had to be estimated. As a next step, high resolution bacterial measurements following CSO discharge events are planned in order to develop a concise picture of processes determining bacterial concentrations at the Ruhr River.