The Semantic Asset Administration Shell

Abstract

The disruptive potential of the upcoming digital transformations for the industrial manufacturing domain have led to several reference frameworks and numerous standardization approaches. On the other hand, the Semantic Web community has made significant contributions in the field, for instance on data and service description, integration of heterogeneous sources and devices, and AI techniques in distributed systems. These two streams of work are, however, mostly unrelated and only briefly regard each others requirements, practices and terminology. We contribute to closing this gap by providing the Semantic Asset Administration Shell, an RDF-based representation of the Industrie 4.0 Component. We provide an ontology for the latest data model specification, created a RML mapping, supply resources to validate the RDF entities and introduce basic reasoning on the Asset Administration Shell data model. Furthermore, we discuss the different assumptions and presentation patterns, and analyze the implications of a semantic representation on the original data. We evaluate the thereby created overheads, and conclude that the semantic lifting is manageable, also for restricted or embedded devices, and therefore meets the needs of Industrie 4.0 scenarios.

Generic Digital Twin Architecture for Industrial Energy Systems

“…In its current state, the concept of the AAS meta-model can be applied and implemented in line with our proposed architecture. It was already shown, that the defined data model of the AAS can be semantically lifted to a knowledge representation based on RDF [41]. In the context of our proposed architecture, this enables the representation of the AAS inside the Shared Knowledge base.…”

Semantic Asset Administration Shells in Industry 4.0: A Survey

“…Similarly, Bader et al introduced Semantic AASs in more detail [13]. In their paper, the authors filled the gap between industrial reference frameworks and semantic description of the physical world.…”

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

“…Existing literature mainly focuses on the development of architectures for addressing lower level interoperability challenges of Industry 4.0, such as distributed storage, data aggregation, and service orchestration (Pisching et al, 2018 ; Bicocchi et al, 2019 ; Fraile et al, 2019 ) as well as big data infrastructures (Pedone and Mezgár, 2018 ; Calabrese et al, 2020 ). A considerable amount of research has also focused on architectures for CPS, digital twins, and AAS (Lee et al, 2015 ; Bader and Maleshkova, 2019 ; Bousdekis et al, 2020a ; Cavalieri and Salafia, 2020 ). For more details, the reader may refer to Moghaddam et al ( 2018 ), Cheng et al ( 2018 ), Fraile et al ( 2019 ), and Zeid et al ( 2019 ).…”

“…Existing related literature usually develops data models and ontologies based on the FMECA background (Zhou et al, 2015 ; Guillén et al, 2016 ; Nunez and Borsato, 2017 ; Ali and Hong, 2018 ; Hribernik et al, 2018 ). However, their deterministic and static nature creates obstacles to the full exploitation of big data in the frame of Industry 4.0 (Bader and Maleshkova, 2019 ). The PR-OWL can enable the representation of domain knowledge enhanced by data analytics in the form of uncertain relationships between the FMECA entities, e.g., failure causes, the failure modes, and the mitigating actions, while the root causes are mapped to the available sensors that serve as indirect indicators of the failure modes.…”