Open Access

Condition Monitoring for mechanical systems like bearings or transmissions is often done by analysing frequency spectra obtained from accelerometers mounted to the components under observation. Although this approach gives a high amount on information about the system behaviour, the interpretation of the resulting spectra requires expert knowledge, that is, a deep understanding of the effect on condition deterioration on the measured spectra. However, an increasing number of condition monitoring applications demands other representations of the measured signals that can be easily interpreted even by non–experts. Therefore, the objective of this paper is to develop an approach for processing measured process data in order to obtain an easy to interpret measure for assessing the component condition. The main idea is to evaluate the deterioration of a component condition by computing the correlation function of current measurements with past measurements in order to detect a component condition deterioration from a change in these correlation functions. Besides the simplicity of the obtained measure, this approach opens the opportunity for integrating a model based approach as well. The developed method is tested based on a condition monitoring application in a roller chain.

The superior electrical and thermal properties of silicon carbide (SiC) allow further shrinking of the active area of future power semiconductor devices. A lower boundary of the die size can be obtained from the thermal impedance required to withstand the high power dissipation during a short-circuit event. However, this implies that the power distribution is homogeneous and that no current filamentation has to be considered. Therefore, this work investigates this assumption by evaluating the stability of a SiC-MOSFET over a wide range of operation conditions by measurements up to destruction, thermal simulations, and high-temperature characterization.

Induced by a societal decision to phase out conventional energy production - the so-called Energiewende (energy transition) - the rise of distributed generation acts as a game changer within the German energy market. The share of electricity produced from renewable resources increased to 31,6% in 2015 (UBA, 2016) with a targeted share of renewable resources in the electricity mix of 55%-60% in 2035 (RAP, 2015), opening perspectives for new products and services. Moreover, the rapidly increasing degree of digitization enables innovative and disruptive business models in niches at the grid's edge that might be the winners of the future. It also stimulates the market entry of newcomers and competitors from other sectors, such as IT or telecommunication, challenging the incumbent utilities. For example, virtual and decentral market places for energy are emerging; a trend that is likely to speed up considerably by blockchain technology, if the regulatory environment is adjusted accordingly. Consequently, the energy business is turned upside down, with customers now being at the wheel. For instance, more than one-third of the renewable production capacities are owned by private persons (Trendsearch, 2013). Therefore, the objective of this chapter is to examine private energy consumer and prosumer segments and their needs to derive business models for the various decentralized energy technologies and services. Subsequently, success factors for dealing with the changing market environment and consequences of the potentially disruptive developments for the market structure are evaluated.

Besides the optimisation of the car, energy-efficiency and safety can also be increased by optimising the driving behaviour. Based on this fact, a driving system is in development whose goal is to educate the driver in energy efficient and safe driving. It monitors the driver, the car and the environment and gives energy-efficiency and safety relevant recommendations. However, the driving system tries not to distract or bother the driver by giving recommendations for example during stressful driving situations or when the driver is not interested in that recommendation. Therefore, the driving system monitors the stress level of the driver as well as the reaction of the driver to a given recommendation and decideswhether to give a recommendation or not. This allows to suppress recommendations when needed and, thus, to increase the road safety and the user acceptance of the driving system.

Stress is becoming an important topic in modern life. The influence of stress results in a higher rate of health disorders such as burnout, heart problems, obesity, asthma, diabetes, depressions and many others. Furthermore individual’s behavior and capabilities could be directly affected leading to altered cognition, inappropriate decision making and problem solving skills. In a dynamic and unpredictable environment, such as automotive, this can result in a higher risk for accidents. Different papers faced the estimation as well as prediction of drivers’ stress level during driving. Another important question is not only the stress level of the driver himself, but also the influence on and of a group of other drivers in the near area. This paper proposes a system, which determines a group of drivers in a near area as clusters and it derives the individual stress level. This information will be analyzed to generate a stress map, which represents a graphical view about road section with a higher stress influence. Aggregated data can be used to generate navigation routes with a lower stress influence to decrease stress influenced driving as well as improve road safety.