Sensors

The double transformation describes the necessary change in the economy in the dimensions of ecology and digitalization. Motion capture systems offer new possibilities for recording and analyzing work processes in industrial assembly. They visualize motion sequences with high frequency, precision and resolution. The question therefore arises as to how the technology can be used in the context of digital transformation to further develop the analysis of work processes and the design of workplaces. Our article discusses this on the basis of solution principles and describes implementation principles for the development of upcoming digital assistance systems.

In recent years, digital platforms have established themselves as a central concept in the IT field. Due to the wide variety of digital platforms available on the market, there is still a need for clear comparison with criteria to enable interested parties to select, change, operate and further develop these platforms. The following paper aims to contribute to the facilitation of this comparison by undertaking a systematic literature review of digital platform frameworks in the context of the Industrial Internet of Things (IIOT) for manufacturing companies and thus providing a basis for a number of potential ways to effectively compare current digital platforms and ecosystems.

This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded data. A machine learning tool is trained with historical wear curves and used to generate the prediction. The training data is collected through statistical tests in which the influencing variables and characteristic curves of different types of wear are identified.

In process engineering, one thinks of production operations that are controlled or regulated by sensors and actuators. And any realization of matter transformation is based on a physical substratum, which holds equally for living systems and their behaviour. The article distinguishes between three system levels: the functional level, the interface to the environment and the cognitive level of. Using these three levels, the learning cycle or the previous Cognitive Loop can be very well illustrated. If one compares with this way of distinction the Bio-Informatization of human intelligence with the technical development stages of mechanization, automation, regulation and deep learning, then the cybernetic-sociological term “operational closure” becomes understandable. It becomes obvious that in the context of a digitized culture of production and organization, we should be prepared for a new kind of cognitive loop based on silicon (SI), an intelligent system behavior via ...

The growing amount of available data due to the digitalization of value creation is accelerating the transformation of manufacturing industries into providers of customer-oriented services. Smart services, currently the most highly developed level of data-based digital services to complement physical products for specific customer expectations, are an example of this. However, the analysis of expert interviews as well as of use cases from business practice shows that the knowledge of how such smart services can be developed is still rudimentary. This article presents a framework for Smart Service Lifecycle Management that supports the systematic development of Smart Services, taking into account business models and the value network. The framework concept will be implemented and validated based on an application example from the textile industry.