Applying knowledge bases to make factories smarter

Ocker, Felix; Paredis, Christiaan J. J.; Vogel‐Heuser, Birgit

doi:10.1515/auto-2018-0138

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…Semantic Web technologies have been extensively applied to support various industrial applications in the last decade ranging from combining sensor networks with the Web [6] to augmenting products with semantic descriptions [33] or enabling smart city infrastructures [5]. The application of these technologies has been taking place in a variety of (mission-critical) domains, such as manufacturing [2,27], electric grids [18], or buildings [4] and addressed tasks both during the engineering (e.g., engineering model integration, digital twin model consistency management) and operation&maintenance (monitoring and anomaly detection, optimization and reconfiguration) phases of cyber-physical systems in such domains [35]. Furthermore, an important task is the verification of technical information objects (e.g., ontologies, semantic data sets) in terms of complying with constraints specified in languages such as SHACL, SWRL, or SPARQL.…”

Section: Application Of Semantic Web Technologies In Industrial Settingsmentioning

confidence: 99%

Deriving semantic validation rules from industrial standards: An OPC UA study

Bareedu,

Frühwirth,

Niedermeier

et al. 2024

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Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards in terms of their compliance with the modeling constraints prescribed by the guidelines. This raises the risk of costly interoperability errors induced by the incorrect use of the standards. There is, therefore, an increased interest in automatic semantic validation of information objects based on industrial standards. In this paper we focus on an approach to semantic validation by formally representing the modeling constraints from unstructured documents as explicit, machine-actionable rules (to be then used for semantic validation) and (semi-)automatically extracting such rules from pdf documents. While our approach aims to be generically applicable, we exemplify an adaptation of the approach in the concrete context of the OPC UA industrial standard, given its large-scale adoption among important industrial stakeholders and the OPC UA internal efforts towards semantic validation. We conclude that (i) it is feasible to represent modeling constraints from the standard specifications as rules, which can be organized in a taxonomy and represented using Semantic Web technologies such as OWL and SPARQL; (ii) we could automatically identify modeling constraints in the specification documents by inspecting the tables ( P = 87 %) and text of these documents (F1 up to 94%); (iii) the translation of the modeling constraints into formal rules could be fully automated when constraints were extracted from tables and required a Human-in-the-loop approach for constraints extracted from text.

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Section: Application Of Semantic Web Technologies In Industrial Settingsmentioning

confidence: 99%

Deriving semantic validation rules from industrial standards: An OPC UA study

Bareedu,

Frühwirth,

Niedermeier

et al. 2024

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show abstract

“…This was the impetus for the use of Semantic Web technologies in industrial databases and knowledge of information search engines, including logical integration into the industrial Internet of Things (IIoT). It is the ontologies focused on the organisation and implementation of IIoT that meet new challenges in industry [11] and are currently relevant. In addition, ontological knowledge bases can be used for industrial design tasks in distributed systems and on the Web.…”

Section: Literature Reviewmentioning

confidence: 99%

Ontological support system of managerial decision-making of production tasks for a food enterprise

Lutska¹,

Vlasenko²,

Ladanyuk³

et al. 2022

Tehnìka ta energetika

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Resource and energy efficiency of industrial production, in particular, food production, is a defining requirement that will ensure its functioning without loss of quality and quantity of final products. This is achieved by observing the requirements for the operational parameters of the company’s technological processes and their operational changes. Given the complexity of the functioning of the energy component of the world and Ukraine due to military operations and their consequences, the issue of quality/cost ratio has become more acute. Therefore, for large manufacturing enterprises, the development of systems for supporting management decision-making in accordance with the Industry 4.0 concept becomes relevant. This will contribute to improving the production and economic indicators of the enterprise through coordinated actions of all links of production activities by structuring and processing large amounts of heterogeneous information. The purpose of the study is to develop a decision support system for the task of choosing the structure of an automated control system based on an ontological knowledge base. The developed application ontology uses descriptive logic and is interpreted as part of a digital production double implemented by a single ontological knowledge base and ontological repository. Considering existing international standards, the OWL2 language was chosen for the implementation of the ontological knowledge base. The ontology system architecture contains an ontology server, a Node-Red application, and a user form. A project decision support system that issues recommendations based on requests for the structure of the control system for a technological facility with uncertainties, considering the requirements and restrictions set for each technological process of a food enterprise, reduces the time to choose the appropriate structures, schemes, and methods. Thus, the designer receives the necessary information, supported by knowledge from the subject area, for the synthesis of an effective automated control system. It is also assumed that the ontological system will be expanded by connecting new created applied ontologies that implement related tasks of an industrial enterprise

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“…This results both in an increase in knowledge and its quality [ 19 ]. An in-depth overview of top-level ontologies in the context of smart factories is presented in prior work [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Potential for combining semantics and data analysis in the context of digital twins

Vogel‐Heuser

Ocker

Weiss

et al. 2021

Phil. Trans. R. Soc. A.

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Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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Applying knowledge bases to make factories smarter

Cited by 11 publications

References 38 publications

Deriving semantic validation rules from industrial standards: An OPC UA study

Deriving semantic validation rules from industrial standards: An OPC UA study

Ontological support system of managerial decision-making of production tasks for a food enterprise

Potential for combining semantics and data analysis in the context of digital twins

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