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![Fig. 3. Example Instance Nodes OPC UA Part 4 Figure B.4 (not complete) [1].](profile/Michael-Weyrich/publication/336623537/figure/fig3/AS:868197314076676@1584005778800/Example-Instance-Nodes-OPC-UA-Part-4-Figure-B4-not-complete-1_Q320.jpg)
![Fig. 4. Example B.2.4 -Filter [1].](profile/Michael-Weyrich/publication/336623537/figure/fig4/AS:868197314080773@1584005778841/Example-B24-Filter-1_Q320.jpg)
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Context 1
... is necessary because an expressive query language also needs a lot of resources, which might not be available on all OPC UA devices. Figure 2 shows such an architecture, which allows connecting devices without the necessary resources for query to a device with a query-engine. On the left side of the picture several clients/apps are depicted, which want to query the information model. ...
Context 2
... the left side of the picture several clients/apps are depicted, which want to query the information model. Our prototype (the middle part of Figure 2) offers two different query languages for clients: SPARQL and OPC UA Query. Internal we use only the SPARQL query language and therefore have to translate OPC UA queries to SPARQL queries. ...
Context 3
... categorize OPC UA information models into two parts: The static part like the Type-Hierarchy, which is translated into triples and after that stored in the triplestore; The dynamic part like the Value-Attribute of a VariableNode, which is fetched on demand directly from the aggregating OPC UA server. The aggregated OPC UA AddressSpace is synchronized with the underlying devices (which could also be another query application, see also Figure 2 right side) and offers access to the OPC UA graph, including live data for Node-Attributes. Nevertheless, static in this context only means that the static data is transformed into triples and synchronized with the triplestore. ...
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Citations
... Schiekofer and Weyrich address in [10] the gap in querying OPC UA information models, noting the absence of a public implementation for such queries and emphasizing the need for an open interface to facilitate vendor-independent models. They argue that efficient query capability is crucial for advancing I4.0. ...
Future Industry 4.0 systems will make use of independent and cooperative assets. In order to use the services of the assets, a digital representation must be available. The Industry 4.0 Asset Administration Shell fulfils this task. It contains all the properties and capabilities of the asset. Currently, retrieving related information over HTTP/REST is complicated because there is no query interface. We propose the development of a universal query interface based on the existing API definition for the Asset Administration Shell. Different approaches are discussed and a conceptual approach using GraphQL is presented.
... OPC UA is considered, because it is one of the most widespread communication protocols in production technology. Ontologies based on the Web Ontology Language (OWL) standard can provide formal semantics and better search functionality compared to OPC UA models (Schiekofer and Weyrich, 2019); thus, we consider a transformation of OPC UA information models to the ontology-based knowledge management system. Subsequently, we examine the connection of the knowledge management system to already existing control logic or process chains (C). ...
The increasing product variability and lack of skilled workers demand for autonomous, flexible production. Since assembly is considered a main cost driver and accounts for a major part of production time, research focuses on new technologies in assembly. The paradigm of Line-less Mobile Assembly Systems (LMAS) provides a solution for the future of assembly by mobilizing all resources. Thus, dynamic product routes through spatiotemporally configured assembly stations on a shop floor free of fixed obstacles are enabled. In this chapter, we present research focal points on different levels of LMAS, starting with the macroscopic level of formation planning, followed by the mesoscopic level of mobile robot control and multipurpose input devices and the microscopic level of services, such as interpreting autonomous decisions and in-network computing. We provide cross-level data and knowledge transfer through a novel ontology-based knowledge management. Overall, our work contributes to future safe and predictable human-robot collaboration in dynamic LMAS stations based on accurate online formation and motion planning of mobile robots, novel human-machine interfaces and networking technologies, as well as trustworthy AI-based decisions.
... OPC UA is considered, because it is one of the most widespread communication protocols in production technology. Ontologies based on the Web Ontology Language (OWL) standard can provide formal semantics and better search functionality compared to OPC UA models (Schiekofer and Weyrich, 2019); thus, we consider a transformation of OPC UA information models to the ontology-based knowledge management system. Subsequently, we examine the connection of the knowledge management system to already existing control logic or process chains (C). ...
The increasing product variability and lack of skilled workers demand for autonomous, flexible production. Since assembly is considered a main cost driver and accounts for a major part of production time, research focuses on new technologies in assembly. The paradigm of Line-less Mobile Assembly Systems (LMAS) provides a solution for the future of assembly by mobilizing all resources. Thus, dynamic product routes through spatiotemporally configured assembly stations on a shop floor free of fixed obstacles are enabled. In this chapter, we present research focal points on different levels of LMAS, starting with the macroscopic level of formation planning, followed by the mesoscopic level of mobile robot control and multipurpose input devices and the microscopic level of services, such as interpreting autonomous decisions and in-network computing. We provide cross-level data and knowledge transfer through a novel ontology-based knowledge management. Overall, our work contributes to future safe and predictable human-robot collaboration in dynamic LMAS stations based on accurate online formation and motion planning of mobile robots, novel human-machine interfaces and networking technologies, as well as trustworthy AI-based decisions.
... OPC UA is a middleware communication protocol developed by the OPC Foundation for industrial communication, which also provides a semantic information space with extendable information models. The aim of OPC UA is to improve interoperability on the transport and semantic layer in typical IoT scenarios [38]. ...
... Semantic interoperability is mainly achieved by the graph-based OPC UA information model combined with Companion Specifications [38]. The OPC UA information model is organized by nodes and references, which enhances the data semantics. ...
Industry 4.0 is helping to unleash a new age of digitalization across industries, leading to a data-driven, inter-operable, and decentralized production process. To achieve this major transformation, one of the main requirements is to achieve interoperability across various systems and multiple devices. Ontologies have been used in numerous industrial projects to tackle the interoperability challenge in digital manufacturing. However, there is currently no semantic model in the literature that can be used to represent the industrial production workflow comprehensively while also integrating digitalized information from a variety of systems and contexts. To fill this gap, this paper proposed industrial production workflow ontologies (InPro) for formalizing and integrating production process information. We implemented the 5 M model (manpower, machine, material, method, and measurement) for InPro partitioning and module extraction. The InPro comprises seven main domain ontology modules including Entities, Agents, Machines, Materials, Methods, Measurements, and Production Processes. The Machines ontology module was developed leveraging the OPC Unified Architecture (OPC UA) information model. The presented InPro ontology was further evaluated by a hybrid combination of approaches. Additionally, the InPro ontology was implemented with practical use cases to support production planning and failure analysis by retrieving relevant information via SPARQL queries. The validation results also demonstrated that using the proposed InPro ontology allows for efficiently formalizing, integrating, and retrieving information within the industrial production process context.
... OPC UA is considered, because it is one of the most widespread communication protocols in production technology. Ontologies based on the Web Ontology Language (OWL) standard can provide formal semantics and better search functionality compared to OPC UA models (Schiekofer and Weyrich, 2019); thus, we consider a transformation of OPC UA information models to the ontology-based knowledge management system. Subsequently, we examine the connection of the knowledge management system to already existing control logic or process chains (C). ...
The increasing product variability and lack of skilled workers demand for autonomous, flexible production. Since assembly is considered a main cost driver and accounts for a major part of production time, research focuses on new technologies in assembly. The paradigm of Line-less Mobile Assembly Systems (LMAS) provides a solution for the future of assembly by mobilizing all resources. Thus, dynamic product routes through spatiotemporally configured assembly stations on a shop floor free of fixed obstacles are enabled. In this chapter, we present research focal points on different levels of LMAS, starting with the macroscopic level of formation planning, followed by the mesoscopic level of mobile robot control and multipurpose input devices and the microscopic level of services, such as interpreting autonomous decisions and in-network computing. We provide cross-level data and knowledge transfer through a novel ontology-based knowledge management. Overall, our work contributes to future safe and predictable human-robot collaboration in dynamic LMAS stations based on accurate online formation and motion planning of mobile robots, novel human-machine interfaces and networking technologies, as well as trustworthy AI-based decisions.
... Queries can be used to collect the relevant data to perform essential evaluations. However, there are currently no query tools available for extracting information from OPC UA documents [7]. ...
... However, this mapping does not consider every single concept of OPC UA, such as ReferenceType. In [17] it is pointed out that Schiekofer et al. in [7] presented the first complete formal mapping between OPC UA and RDF. Their work shows that a trivial mapping between OPC UA and OWL DL is not possible because the expressiveness of OWL DL is not sufficient to represent the entire OPC UA information model. ...
... OPC UA models could now be used in conjunction with the RDF/OWL toolbox to scrutinize them with regard to consistency and validity. The authors also showed how SPARQL can be used as a capable query language for nodesets [6]. Perzylo et al. [7] published a repository of ontologies along the entire scope of OPC UA. ...
... Schiekafer et al. [18] OPC UA 7 Solution Schiekafer et al. [17] OPC UA 5 Solution Bakakeu et al. [1] OPC UA 5 Solution Huan and Hein [8] AutomationML 4 Solution Steindl et al. [20] OPC UA 3 Solution Patzer et al. [14] OPC UA 3 Evaluation Huan and Hein [7] AutomationML 2 Solution Majumder et al. [13] OPC UA 1 Evaluation Katti et al. [9] OPC UA 1 Solution Huan and Hein [5] AutomationML 1 Solution Katti et al. [10] OPC UA 0 Solution Huan and Hein [6] AutomationML 0 Solution Table 2 describes an interesting view from the citation perspective. The most cited articles correspond to the OPC UA standard, followed by AutomationML, or a combination of the previous ones. ...
... Schiekofer and Weyrich [18] present how the OPC UA model can be queried. The proposal is based on the ontological representation of OPC UA specified in [17]. ...
... The articles [9,10,12] used SWRL for specifying business rules (19% of contributions). Finally, 6 proposals (38% of retained articles) [1,12,13,15,18,20] employed SPARQL for retrieving knowledge from the data sources. ...
Industry 4.0 requires standardized information models for heterogeneous platform integration. Two standards for interoperability are IEC 62541-a machine-to-machine communication protocol expressed in OPC Unified Architecture (OPC UA) format-and IEC 62714 to describe production plants or plant components expressed in AutomationML format. Despite their wide adoption, these languages lack formal semantics for automatic data interpretation. This work describes a Systematic Mapping Study focused on recent studies on the transformation between the OPC UA and AutomationML languages and OWL or RDF. Articles on conferences and journals from 2015 onwards are queried and analyzed from the Scopus, IEEE, and ACM databases. The study is limited to articles written in the English language and published in journals. From 43 documents, 16 are retained following the inclusion and exclusion criteria. As a result of this study, the main challenges facing Industry 4.0 are identified. These are the lack of support for the ontology engineering tasks performed by the knowledge engineer, the representation of the semantic digital twin, data duplication, and communication overhead. Germany concentrates 87 % of the involved articles on the subject.
... Because of its capabilities, OPC UA is a suitable technology for Industry 4.0 applications. A disadvantage of the OPC UA information model is its lack of formal semantics, the missing browsing capability [2] and a limited semantic expressiveness compared to more advanced knowledge representations [3]. However, these features are important to develop more sophisticated CPSs, like presented in [4], which are able to get an in-depth knowledge of the monitored system or make them self-configurable and self-adaptive. ...
... Many solutions for automated reasoning on OPC UA information models transform the OPC UA information model into a web ontology in order to take advantage of existing powerful ontology reasoners [4]- [7]. However, most web ontology technologies and reasoners do not thrive well at the plant floor level because they were simply not designed to meet the performance and scalability requirements of modern industrial control systems. ...
With the fourth industrial revolution, the OPC Unified Architecture (OPC UA) [1] has emerged as the standard communication framework for the implementation of cyber-physical production systems since it can be used for both, communication and information modeling. Even though OPC UA helps bridge the interoperability gap at the automation level, its semantic has not yet been formally defined and generated or manually created OPC UA information models are often incomplete and inconsistent making an efficient automated reasoning and knowledge inference on the OPC UA address space particularly challenging. In this paper, we show that it is possible to train a machine learning system on OPC UA information models, such that it performs automated reasoning over OPC UA knowledge graphs with high precision and recall. More specifically, we present a reinforcement learning-based solution that learns to reason on semantically incomplete OPC UA information models by constructing multi-hop relational paths along with an embedded vector space of the knowledge graph representing the information model. The construction of such relational paths allows the discovery of missing relations between the entities and at the same time the evaluation of the truth of the encoded triples, thus enabling consistency checks and questions answering.
