On a Uniform Causality Model for Industrial Automation

The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning

A Research Agenda for AI Planning in the Field of Flexible Production Systems

Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived

An efficient, repetitive nanosecond pulsed power generator with ten synchronized spark gap switches

This paper describes an efficient, repetitive nanosecond pulsed power generator using a Transmission-Line-Transformer (TLT) based multiple-switch technology. Within this setup, a 10-stage TLT and ten high-pressure spark-gap switches are adopted. At the input side, ten spark-gap switches are interconnected in series via the TLT, so that all the spark-gap switches can be synchronized automatically. At the output side, all the stages of the TLT are connected in parallel, thus a low output impedance (5 ¿) is obtained, and a large output current is realized by adding the currents through all the switches. Experimental results show that 10 spark-gap switches can be synchronized within about 10 ns. The system has been successfully demonstrated at repetition rates up to 300 pps (Pulses Per Second). Pulses with a rise-time of about 11 ns, a pulse width of about 55 ns, an energy of 9-24 J per pulse, a peak power of 300-810 MW, a peak voltage of 40-77 kV, and a peak current of 6-11 kA have been achieved with an energy conversion efficiency of 93-98