Software Engineering Integration For Flexible Automation Systems

Linked Data in Automation Systems Engineering

Marta Sabou, Fajar J. Ekaputra, Olga Kovalenko, Stefan BifflInstitute of Software Technology and Information System.

Vienna University of Technologyhttp://cdl.ifs.tuwien.ac.at

Christian Doppler Laboratory

Cyber-Physical Systems (CPS)

Flexible, adaptive manufacturing (CPPS) Smart, distributed transportation systems

Software Eng.Mechanical Eng. Electrical Eng.

CPS Engineering Phase

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Problem Setting:Multi-disciplinary Engineering

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AutomationML: Industrie4.0 standard

AutomationML consortium: ABB, Daimler, Kuka, Siemens, Volkswagen ….

Open, neutral, XML based, and free industry data representation standard which enables a domain and company spanning transfer of engineering data

Credits: www.automationml.org

Problem Overview

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① Complex data structure with intricate links between disciplines② Integration of AutomationML files from different disciplines important ③ Limited support for cross-disciplinary analytics ④ Limited options for platform independent browsing of AutomationML data

Solution: AutomationML Analyzer

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Relies on Linked Data technologies to enable efficient integration, browsing, querying, and analysis of diverse mechatronic engineering models represented in the AutomationML.

Browser based visualisation

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Browsable internal links

Different Views on Data

Querying Integrated AutomationML Data

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Predefined SPARQL queries enable monitoring, analysis, validation and defect detection tasks

Take Home Message

Multi-disciplinary engineering (MDE)– Representative for CPS and Industrie4.0– Complex setting

AutomationML– Data exchange format for mechatronic engineering data

Linked Data benefits for MDE:– Ontology-based integration of diverse engineering models– Helping to make implicit connections explicit– Linking to external Web resources (Ecl@ss)– Creation of browser-based visualizations– Query supported analysis of cross-disciplinary data

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Thank you