Former Projects

Linked Forever Young Production Automation with Active Components

DFG Priority Programme 1593/2 (In cooperation with HSU, Hamburg)

Abstract

In order to address software degeneration problems in long-living production automation systems, the FYPA²C project of the first phase of the priority programme introduced an anti-aging cycle that perpetually reinforces the consistency of software specifications and their actual behavior in operation. Project results of this phase included support for operators during undocumented software evolution based on methods and processes for gathering knowledge about production processes and their properties. In this approach, a knowledge carrying software checks desired pre-evolution knowledge constantly against the actual observable system behavior in order to detect evolutionary changes as a prerequisite to prevent software degeneration.

However, due to the complexity of realistic plant dynamics, evolution support in the operational phase is still rather limited w.r.t, e.g., automatic detection of evolution potential or predictions of evolutionary effects. To overcome this limitation, future production automation systems should be situated in an evolution environment which is not just aware of its properties, but also aware of its evolution potentials. As an opportunity to turn into evolution-aware platforms, nowadays production systems are already increasingly becoming parts of industrial networks. Consequently, new potential for cooperation support arise, because evolution steps have likely already been carried out on a similar system within a similar context.

Therefore, the new LinkedFYPA²C project proposes to exploit such experiences inherent contained in a network by envisioning an associated community of coevolving systems with the following main contributions to software evolution. (1) Development of an interpretable knowledge base to describe evolution processes of a Knowledge Carrying Software. (2) Methods and processes to exchange evolution experiences in a Machine-to-Machine communication in order to assess already performed evolution steps of coevolved systems and predict their impacts with context-related reasoning techniques. (3) A robust and flexible runtime platform and middleware for evolution which allows continuous anticipation and reflection of evolutionary changes by cooperatively detecting evolution potential to proactively trigger knowledge exchange. Together, these contributions provide an autonomous and decentralized management of collective experiences about evolution to establish the networked neighborhood of production systems as an active evolution environment. This will allow for a human assisting evolution support by presenting context-related evolution steps and their predicted property changes to human operators in order to achieve a better guided, foreseeable and less risky evolution for networked (production) systems.

8. October 2024 at 11:21 by Prof. Dr. Winfried Lamersdorf

2019		Christopher Haubeck, Winfried Lamersdorf, et al. Learning from Evolution for Evolution In: Managed Software Evolution
		Winfried Lamersdorf, Christopher Haubeck, et al. Maintaining Security in Software Evolution In: Managed Software Evolution
2018		Christopher Pietsch, Udo Kelter, Christopher Haubeck, Winfried Lamersdorf, Abhishek Chakraborty, Alexander Fay Using Model Differencing to reason about Observable Behaviour Changes of Manufacturing Systems In: AT - Automatisierungstechnik
		Christopher Haubeck, Heiko Bornholdt, Winfried Lamersdorf, Abhishek Chakraborty, Alexander Fay Step-based Evolution Support among Networked Production Automation Systems In: AT - Automatisierungstechnik
		Christopher Haubeck, Winfried Lamersdorf, Abhishek Chakraborty, Alexander Fay, et al. Learning from Evolution for Evolution In: DFG SPP1593 - Book of Results
		Christopher Haubeck, Jan Ladiges, Winfried Lamersdorf, Abhishek Chakraborty, Alexander Fay, et al. Maintaining Security in Software Evolution In: DFG SPP1593 - Book of Results
		Christopher Haubeck, Winfried Lamersdorf, Alexander Fay A Knowledge Carrying Service-Component Architecture for Smart Cyber Physical Systems: An Example based on self-documenting production systems In: ICSOC Workshops 2017
2017		Kim Reichert, Alexander Pokahr, Till Hohenberger, Christopher Haubeck, Winfried Lamersdorf A Taxonomy of Anomalies in Distributed Cloud Systems: The CRI-Model In: Intelligent Distributed Computing XI
		Christopher Haubeck, Abhishek Chakraborty, Jan Ladiges, Alexander Pokahr, Winfried Lamersdorf, Alexander Fay Evolution of Cyber-Physical Production Systems supported by community-enabled experiences In: IEEE 15th International Conference of Industrial Informatics INDIN 2017

2019		Master Thesis of Florian Abt Monitoring-Dienste im Web of Things Tutors: Heiko Bornholdt, Philipp Kisters Supervisors: Winfried Lamersdorf, Dirk Bade
		Bachelorarbeit of Michael Hirsch Evaluierung des Einsatzes von Question-Answering-Systemen für Sensornetzdaten Tutor: Heiko Bornholdt Supervisors: Winfried Lamersdorf, Mareike Schmidt
2018		Master Thesis of Danny Schubert Autonome Mustererkennung durch maschinelles Lernen zur Prädiktion von Sensorausfällen in Transportfahrzeugen Supervisors: Winfried Lamersdorf, Dirk Bade
2017		Master Thesis of Dadssi Nour-Eddine Generierung von Palladio Performancemodellen in iterativen Entwicklungsprozessen Tutor: Christopher Haubeck Supervisors: Winfried Lamersdorf, Alexander Pokahr

2024		Dissertation of Heiko Bornholdt Towards Intent-Based Overlay Networking Tutor: Winfried Lamersdorf
2019		Dissertation of Christopher Haubeck Evolutionsunterstützung in Cyber-Physischen Systemen Tutors: Winfried Lamersdorf, Alexander Fay
2018		Dissertation of Jan Ladiges Automatisierte Bestimmung von Eigenschaften industrieller Produktionssysteme unter Einfluss evolutionärer Änderungen Supervisors: Alexander Fay, Winfried Lamersdorf