This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes the user’s declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration.

No abstract

The planning, testing and integration of modern automation systems is becoming more and more a bottleneck in the construction of new production facilities. This is due to the facts that plants grow in complexity and that modern automation systems are highly distributed and comprise complex components. To cope with these challenges and to guarantee short implementation times and a small number of errors for the automation systems, modern development processes are needed. Such modern processes can be reduced to four main aspects: (i) A seamless process with corresponding seamless tools, (ii) a high level of model reuse and adaptability, (iii) executable models and early tests, and (iv) a system-wide planing process of the distributed system. Therefore, the established tool landscape with its specialized tools for each discipline of engineering has difficulties to keep up with these trends. The approach presented in this paper implements a development process including the aspects (i) - (iv) using the new data exchange format AutomationML. AutomationML serves as an enabling technology and has the potential to change future development processes and may trigger the development of new, better integrated tools.(1

Today's production plants are not conceivable without automation systems. Due to the increasing complexity of production plants and therefore of automation systems, delays and interruptions in automation projects are observed. A design model for more efficient planning of industrial automation systems is introduced. It is based on a new and practical proceeding for the construction of a requirements model. Extended feature models as formal requirements representation enable to verify the consistency of requirements. New insights on the necessary capabilities of a formal reasoning system for planning the whole automation system are deduced from the design model.

Nowadays, model-driven formal verification approaches are much researched for testing industrial control applications. However, these approaches can't check the system for bus errors, faulty hardware configurations and compatibility problems efficient. For sufficient testing, Hardware-in-the-Loop (HIL) test should be used. But the HIL-test is rarely used in the field of automation because of missing experience with it and great costs by using vendor-dependent solutions. This paper will discusses the current problems of HIL-tests for verifying the control applications and presents a promising vendor-independent approach for its implementation