Ontology-based validation of plant models

Abele, Lisa; Legat, Christoph; Grimm, Stephan; Müller, Andreas W.

doi:10.1109/indin.2013.6622888

Cited by 25 publications

(11 citation statements)

References 13 publications

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“…Ontohub ontology repository is a platform that can support such alignment and consistency checking of linked ontologies, by exploiting the parsing and inference back end of the Heterogeneous Tool Set [26]. Finally, the potentiality of reasoning will be investigated to assess the integrity and consistence of a production system model, as for example addressed by Abele et al [27].…”

Section: Discussionmentioning

confidence: 99%

Ontology-based modeling of production systems for design and performance evaluation

Terkaj

Urgo

2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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This paper presents the extension to an ontology-based data model supporting the design and performance evaluation of production systems. This extension aims to link the modeling of the spatial representation of physical objects with the characterization of their states and behavior. Furthermore, the formalization of the performance history of a production system and its components is addressed to capture both the spatial and state evolution of the objects. Such history can be provided by simulation runs or gathered from a monitoring system. A test case is described and then used to show how different software tools can be integrated to support the integrated design and evaluation of a production system

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Section: Discussionmentioning

confidence: 99%

Ontology-based modeling of production systems for design and performance evaluation

Terkaj

Urgo

2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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“…SPARQL queries are executed over the discipline/tool ontologies regarding the defined mappings in order to perform inconsis-tency detection across discipline/tool boundaries. An approach for the automated validation of plant models is presented in [27], where CAEX models are transformed to ontology-based representations and verified through SPARQL queries. In the area of requirements and test case management, Feldmann et al [28] present a modeling approach that enables the early integration of requirements and test cases.…”

Section: Semantic Web Technologies For Defect Detectionmentioning

confidence: 99%

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

Winkler¹,

Sabou²,

Biffl³

2017

Quality Control and Assurance - An Ancient Greek Term Re-Mastered

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In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations.

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“…A model without such contradictions is called consistent [7]. The need for consistency management thus arises if models are created and maintained independently within collaborative projects [8,9]. Within consistency management, rules define conditions evaluated to a truth value [10] and indicate whether a certain consistency is fulfilled or not.…”

Section: Conformance Consistency Compatibilitymentioning

confidence: 99%

Interface Behavior Modeling for Automatic Verification of Industrial Automation Systems' Functional Conformance

Legat

Mund

Campetelli

et al. 2014

At - Automatisierungstechnik

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To facilitate engineering and evolution of automation systems, ensuring the correctness of the design models is an important topic. Industrial automation systems are composed of various heterogeneous elements designed by different disciplines such as mechanical, electrical/electronic and software engineering. In this contribution, an approach for modeling industrial automation systems is presented which is based on interface behavior modeling of design artifacts and which supports automatic verification of their functional conformance while considering information from various disciplines.

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Ontology-based validation of plant models

Cited by 25 publications

References 13 publications

Ontology-based modeling of production systems for design and performance evaluation

Ontology-based modeling of production systems for design and performance evaluation

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

Interface Behavior Modeling for Automatic Verification of Industrial Automation Systems' Functional Conformance

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