In the engineering and manufacturing domain, there is currently an atmosphere of departure to a new era of digitized production. In different regions, initiatives in these directions are known under different names, such as industrie du futur in France, industrial internet in the US or Industrie 4.0 in Germany. While the vision of digitizing production and manufacturing gained much traction lately, it is still relatively unclear how this vision can actually be implemented with concrete standards and technologies. Within the German Industry 4.0 initiative, the concept of an Administrative Shell was devised to respond to these requirements. The Administrative Shell is planned to provide a digital representation of all information being available about and from an object which can be a hardware system or a software platform. In this paper, we present an approach to developing such a digital representation based on semantic knowledge representation formalisms such as RDF, RDF Schema and OWL. We present our concept of a Semantic I4.0 Component which addresses the communication and comprehension challenges in Industry 4.0 scenarios using semantic technologies. Our approach is illustrated with a concrete example showing its benefits in a real-world use case.
Currently, there are some efforts discussing the need of bringing more semantics and data-driven approaches to I4.0. @cite_22 presents guidelines, aiming to help on choosing the level of semantic formalization for the representation of the different types of I4.0 projects. The crucial role of semantic technologies for mass customization is discussed in @cite_29 . This work recognizes semantic technologies as a glue to connect smart products, data and services. Obitko @cite_21 describes the application of semantics to I4.0 from the Big Data perspective. The features of Big Data, as well as, an ontology for sensor data are presented. tab:RelatedI40ComponentApproaches provides a comparison of our approach to the related I4.0 component description approaches.
@cite_29 @cite_21 @cite_22
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We discuss the key role of semantic technologies for the mass customization of smart products, smart data, and smart services. It is shown that new semantic representation languages for the description of services and product memories like USDL and OMM provide the glue for integrating the Internet of Things, the Internet of Data, and the Internet of Services. Semantic service matchmaking in cyber-physical production systems is presented as a key enabler of the disruptive change in the production logic for Industrie 4.0. Finally, we discuss the platform stack for mass customization and show how a customized smart product can serve as a platform for personalized smart services that are based on smart data provided by the connected product. Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publically available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases targeting semantically-enabled plug-and-produce. Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories. Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. We therefore formulate the multimedia content access problem as a multimedia pattern recognition problem. We propose a probabilistic framework for semantic video indexing, which call support filtering and retrieval and facilitate efficient content-based access. To map low-level features to high-level semantics we propose probabilistic multimedia objects (multijects). Examples of multijects in movies include explosion, mountain, beach, outdoor, music etc. Semantic concepts in videos interact and to model this interaction explicitly, we propose a network of multijects (multinet). Using probabilistic models for six site multijects, rocks, sky, snow, water-body forestry greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this framework to semantic indexing. We demonstrate how detection performance can be significantly improved using the multinet to take interconceptual relationships into account. We also show how the multinet can fuse heterogeneous features to support detection based on inference and reasoning. Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve software engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publicly available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases concerning plug-and-produce.