Band 66, Heft 10 - Special Issue: Methods to support the evolution of Cyber Physical Production Systems / Alexander Fay, Birgit Vogel-Heuser

Continuous integration (CI) is widely used in software engineering. The observed benefits include reduced efforts for system integration, which is particularly appealing for engineering automated production systems (aPS) due to the different disciplines involved. Yet, while many individual quality assurance means for aPS have been proposed, their adequacy for and systematic use in CI remains unclear. In this article, the authors provide two key contributions: First, a quality model for a model-based engineering approach specifically developed for aPS. Based thereon, a discussion of the suitable verification techniques for aPS and their systematic integration in a CI process are given. As a result, the paper provide a blueprint to be further studied in practice, and a research agenda for quality assurance of aPS.

Understanding changes in a manufacturing system is of utmost importance to effectively manage its evolution. This article proposes a pattern-based approach for capturing and describing behavioral changes by integrating recent advantages in the fields of system monitoring and model differencing. Observed changes are described as lifted model differences between two model versions. This helps in explaining observable evolution with a change-first approach.

Control software for automated Production Systems (aPSs) becomes increasingly complex. Respective systems undergo constant evolution. Yet, proper documentation may not always be present, entailing maintenance issues in the long run. While manual examination of software for aPSs is an error-prone task, static analysis can improve system quality. However, it has not been applied to describe software evolution by means of changed systems artifacts. The authors address this issue and perform change analyses on IEC61131-3 projects, identifying introduced and removed systems artifacts as well as existing ones affected. By that, the authors aim to support sustainable evolution. Two feasibility studies, implemented independently, but for the same evolution scenarios for an automation plant, are used for evaluation. The technique is shown to be efficient and highly precise.

Production system operators need support for collecting and pre-processing data on production systems consisting of several system components, as foundation for optimization and defect detection. Traditional approaches based on hard-coded programming of such run-time data collection systems take time and effort, and require both domain and technology knowledge. In this article, we introduce the AML-RTDC approach, which combines the strengths of AutomationML (AML) data modeling and model-driven engineering, to reduce the manual effort for realizing the run-time data collection (RTDC) system. We evaluate the feasibility of the AML-RTDC approach with a demonstration case about a lab-sized production system and a use case based on real-world requirements.

With recent trends in manufacturing automation, control software in automated production systems becomes more complex and has more variability to keep pace with customer and market requirements. Quality assurance also becomes more and more important to ensure that the systems live up to expectations. However, correctness of automation software is rarely verified using formal techniques in spite of their high coverage. One of the main reasons is the lack of specification languages suitable for this application area that are both comprehensible and sufficiently expressive. Generalized test tables (GTTs), which are a specification language for reactive systems, were presented recently as an accessible representation for application engineers. This formalism achieves both the comprehensibility of concrete test tables and the coverage of formal methods. In our approach, the specification provided by GTTs is used for formal verification, especially model checking. In this paper, we present four new features for GTTs: the progression flag, strong repetition, row grouping, and specification on internal variables. We demonstrate the applicability and evaluate the comprehensibility of GTT-based specification and verification using a range of diverse scenarios from the community demonstrator, the extended Pick & Place Unit.

Production systems are no longer rigid, unyielding, and isolated systems anymore. They are rather interconnected cyber-physical systems with an evolution process that needs to be supported. To enable reusability in evolution, a change-first cooperative support is proposed that relies on model-based evolution steps. The approach establishes a network-wide evolution process in a peer-to-peer networked community. Thus, moving towards decentralised marketplaces for evolution steps.

The oil and gas industry envisions a smarter way of running their business where they can visualize their entire operation, call and retrieve production data effortlessly and seamlessly, collaborating across the entire enterprise to decrease production costs while increasing recovery. The so-called Intelligent Oil Field (IOF) promotes reference architectures and development approaches that improve the flexibility and efficiency of upstream and downstream process. In this sense, OPC UA provides local and remote access to plant information, allowing horizontal and vertical integration in a reliable, safe and efficient way. This article contributes an open IOF architecture for vertical integration based on cyber-physical production systems, configured under IEC 61499 and using OPC UA. Therefore, the proposal is suitable to achieve flexible manufacturing.