Linked Data as Integrating Technology for Industrial Data

Graube, Markus; Pfeffer, Johannes; Ziegler, Jens; Urbas, Leon

doi:10.1109/nbis.2011.33

Cited by 32 publications

(8 citation statements)

References 13 publications

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“…Based on the work of Graube et al [23] and the approach to create a networked information space for engineering data with Linked Enterprise Data, the presented information models are mapped to RDF to have them available in an information space. RDF allows the models to be mapped closer to their UML model without influences or limitations of the original technological implementation.…”

Section: Information Space and Rdf Descriptionmentioning

confidence: 99%

Efficient Automation Engineering of Modular Process Equipment Assemblies Using the Digital Twin

Rahm

Theißen

Klose

et al. 2021

Chemie Ingenieur Technik

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Modular process plants are a promising approach to accelerate the engineering of greenfield process plants. This is achieved by the combination of pre-engineered process equipment assemblies (PEAs) with a standardized interface for the integration in process control systems, the module type package (MTP). This standardized interface can be integrated in the engineering process to further improve the potential. In this paper existing standards are used to generate aspects of the MTP. For this purpose, the engineering process of conventional plants is adapted for the engineering of PEAs. Therefore, similarities and potential of the described aspects have been analyzed regarding shared fundamental standards and characterized by their location in the asset life cycle. To describe the interconnections of the different standards, exemplary rules are proposed, which can be used for a transformation.

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Section: Information Space and Rdf Descriptionmentioning

confidence: 99%

Efficient Automation Engineering of Modular Process Equipment Assemblies Using the Digital Twin

Rahm

Theißen

Klose

et al. 2021

Chemie Ingenieur Technik

Self Cite

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“…Much research effort is put into semantic description of data to make it machinereadable and especially -understandable. Efforts are going in different directions using linked data approaches [38,39], description of devices like the Industry 4.0 reference architecture (RAMI 4.0) and similar device description approaches [40,41], and various other ways to describe sensors and connected machines [42][43][44]. The most promising seems to use semantics already designed in OPC UA.…”

Section: Semantic Description Of Available Datamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

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“…1) Interoperability: To meet the interoperability demand, RDF and Linked Data have proven to be a successful way to integrate different types of data [17], [18], [19]. Embedded in the semantic for Administrative Shell we propose RDF as a middle layer to support the interoperability between the data of the legacy systems and the data generated by the I4.0 component.…”

Section: Semantic I40 Component a Addressing The Challenges Witmentioning

confidence: 99%

Towards a Semantic Administrative Shell for Industry 4.0 Components

Grangel-González

Halilaj

Coşkun

et al. 2016

2016 IEEE Tenth International Conference on Semantic Computing (ICSC)

108

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In the engineering and manufacturing domain, there is currently an atmosphere of departure to a new era of digitized production. In different regions, initiatives in these directions are known under different names, such as industrie du futur in France, industrial internet in the US or Industrie 4.0 in Germany. While the vision of digitizing production and manufacturing gained much traction lately, it is still relatively unclear how this vision can actually be implemented with concrete standards and technologies. Within the German Industry 4.0 initiative, the concept of an Administrative Shell was devised to respond to these requirements. The Administrative Shell is planned to provide a digital representation of all information being available about and from an object which can be a hardware system or a software platform. In this paper, we present an approach to develop such a digital representation based on semantic knowledge representation formalisms such as RDF, RDF Schema and OWL. We present our concept of a Semantic I4.0 Component which addresses the communication and comprehension challenges in Industry 4.0 scenarios using semantic technologies. Our approach is illustrated with a concrete example showing its benefits in a real-world use case.

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Linked Data as Integrating Technology for Industrial Data

Cited by 32 publications

References 13 publications

Efficient Automation Engineering of Modular Process Equipment Assemblies Using the Digital Twin

Efficient Automation Engineering of Modular Process Equipment Assemblies Using the Digital Twin

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Towards a Semantic Administrative Shell for Industry 4.0 Components

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