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Integrating Production Automation Expert Knowledge Across Engineering Domains

Integrating Production Automation Expert Knowledge Across Engineering Domains

Thomas Moser, Stefan Biffl, Wikan Danar Sunindyo, Dietmar Winkler
Copyright: © 2013 |Pages: 16
ISBN13: 9781466626478|ISBN10: 146662647X|EISBN13: 9781466626782
DOI: 10.4018/978-1-4666-2647-8.ch009
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MLA

Moser, Thomas, et al. "Integrating Production Automation Expert Knowledge Across Engineering Domains." Development of Distributed Systems from Design to Application and Maintenance, edited by Nik Bessis, IGI Global, 2013, pp. 152-167. https://doi.org/10.4018/978-1-4666-2647-8.ch009

APA

Moser, T., Biffl, S., Sunindyo, W. D., & Winkler, D. (2013). Integrating Production Automation Expert Knowledge Across Engineering Domains. In N. Bessis (Ed.), Development of Distributed Systems from Design to Application and Maintenance (pp. 152-167). IGI Global. https://doi.org/10.4018/978-1-4666-2647-8.ch009

Chicago

Moser, Thomas, et al. "Integrating Production Automation Expert Knowledge Across Engineering Domains." In Development of Distributed Systems from Design to Application and Maintenance, edited by Nik Bessis, 152-167. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2647-8.ch009

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Abstract

The engineering of a complex production automation system involves experts from several backgrounds, such as mechanical, electrical, and software engineering. The production automation expert knowledge is embedded in their tools and data models, which are, unfortunately, insufficiently integrated across the expert disciplines, due to semantically heterogeneous data structures and terminologies. Traditional integration approaches to data integration using a common repository are limited as they require an agreement on a common data schema by all project stakeholders. This paper introduces the Engineering Knowledge Base (EKB), a semantic-web-based framework, which supports the efficient integration of information originating from different expert domains without a complete common data schema. The authors evaluate the proposed approach with data from real-world use cases from the production automation domain on data exchange between tools and model checking across tools. Major results are that the EKB framework supports stronger semantic mapping mechanisms than a common repository and is more efficient if data definitions evolve frequently.

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