Architecture for Knowledge Exploration of Industrial Data for Integration into Digital Services

Weskamp, Jan Nicolas; Chowdhury, Arnab; Pethig, Florian; Wiśniewski, Łukasz

doi:10.1109/icps48405.2020.9274700

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Moyne et al [69] studied DT definitions in manufacturing and clustered them into requirements (1) derived from the literature, (2) derived from DTs in use today, and (3) applications in the near future. Weskamp et al [70] formulated requirements for the architecture of a knowledge exploration platform of industrial data for integration into digital services. Steindl et al specified functional and non-functional requirements for a DT service framework derived in a literature review [22] and clustered these requirements based on three RAMI 4.0 layers (information, functional and business).…”

Section: Requirements On Digital Twins In the Energy Sectormentioning

confidence: 99%

“…The DT should build on standardized technologies and use available metadata, hence facilitating model integration, information exchange and maintenance. [70,72] R13 The DT must be able to process heterogeneous data from different sources. [37,61] R14…”

Section: R1mentioning

confidence: 99%

“…The DT should be able to store data with context information and exchange information in a semantically meaningful way. [61,69,70] R15 The DT must allow for safe and secure operation of the physical entity. [75] The need for the accommodation of various types of simulation models (R11) is particularly important in IES, where models of both varying fidelity and different physical considerations are applied, e.g., heat transfer, fluid dynamics, mechanical stress and chemical kinetics.…”

Section: R1mentioning

confidence: 99%

“…While we do not assign a high value to this topic with regard to IES, we argue that basic requirements for the lifecycle aspect in DTs are already covered by various functional requirements in our list (e.g., R6). For more detailed necessities, e.g., on information technology (IT) infrastructure, serviceand model management, we refer to the particular literature presented above, i.e., the work of Weskamp et al [70], Steindl et al [22] and Negri et al [71].…”

Section: R1mentioning

confidence: 99%

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Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

et al. 2022

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Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.

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Section: Requirements On Digital Twins In the Energy Sectormentioning

confidence: 99%

Section: R1mentioning

confidence: 99%

Section: R1mentioning

confidence: 99%

Section: R1mentioning

confidence: 99%

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Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

et al. 2022

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A Digital Twin Platform for Industrie 4.0

et al. 2022

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In an Industrie 4.0 (I4.0), rigid structures and architectures applied in manufacturing and industrial information technologies today will be replaced by highly dynamic and self-organizing networks. Today’s proprietary technical systems lead to strictly defined engineering processes and value chains. Interacting Digital Twins (DTs) are considered an enabling technology that could help increase flexibility based on semantically enriched information. Nevertheless, for interacting DTs to become a reality, their implementation should be based on open standards for information modeling and application programming interfaces like the Asset Administration Shell (AAS). Additionally, DT platforms could accelerate development and deployment of DTs and ensure their resilient operation.This chapter develops a suitable architecture for such a DT platform for I4.0 based on user stories, requirements, and a time series messaging experiment. An architecture based on microservices patterns is identified as the best fit. As an additional result, time series data should not be integrated synchronously and directly into AASs, but rather asynchronously, either via streams or time series databases. The developed DT platform for I4.0 is composed of specialized, independent, loosely coupled microservices interacting use case specifically either syn- or asynchronously. It can be structured into four layers: continuous deployment, shop-floor, data infrastructure, and business services layer. An evaluation is carried out based on the DT controlled manufacturing scenario: AAS-based DTs of products and manufacturing resources organize manufacturing by forming highly dynamic and self-organizing networks.Future work should focus on a final, complete AAS integration into the data infrastructure layer, just like it is already implemented on the shop-floor and business services layers. Since with the standardized AAS only one interface type would then be left in the DT platform for I4.0, DT interaction, adaptability, and autonomy could be improved even further. In order to become part of an I4.0 data space, the DT platform for I4.0 should support global discovery, data sovereignty, compliance, identity, and trust. For this purpose, Gaia-X Federation Services should be implemented, e.g., as cross-company connectors.

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DIPy-AI: Brain-Cognition-Inspired DIKW Pyramid-Based Agile AI Architecture for Industrial Sensor Data Assimilation

Mishra,

Zhong

2024

Studies in Computational Intelligence

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Architecture for Knowledge Exploration of Industrial Data for Integration into Digital Services

Cited by 5 publications

References 8 publications

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

A Digital Twin Platform for Industrie 4.0

DIPy-AI: Brain-Cognition-Inspired DIKW Pyramid-Based Agile AI Architecture for Industrial Sensor Data Assimilation

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