COGNITWIN – Hybrid and Cognitive Digital Twins for the Process Industry

Abburu, Sailesh; Berre, Arne J.; Jacoby, Michael; Roman, Dumitru; Stojanović, Ljiljana; Stojanović, Nenad

doi:10.1109/ice/itmc49519.2020.9198403

Cited by 69 publications

(41 citation statements)

References 9 publications

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“…The conceptual architecture of the Cognitive Twin Toolbox presented in [15] also introduces a "Knowledge Repository" in combination with "Cognitive Services". This is only applied for a "cognitive Twin".…”

Section: Discussionmentioning

confidence: 99%

“…The basic DT concept implements only a so-called "Metadata Repository". A separation into two distinguishable components, as presented in [15] seems neither beneficial nor has practical advantages. Therefore, we introduced the Shared Knowledge base, which acts as a central point of information in our GDTA.…”

Section: Discussionmentioning

confidence: 99%

“…"Cognitive Twin Toolbox"-In [15], a conceptual architecture of the Cognitive Twin Toolbox (COGNITWIN) is presented with a special focus on the process industry. Three levels of twins were defined: A "Digital Twin" which only uses isolated models of the physical system, a "Hybrid Twin" which is also able to interconnect its models, and a "Cognitive Twin" which uses extended models that include expert knowledge for problem-solving and to handle unknown situations.…”

Section: Related Workmentioning

confidence: 99%

“…Details of how the information of the AAS can be exchanged in a meaningful way between partners along a value chain can be found in [17], where a meta-model for the AAS is defined. As resource description, discovery, and access are the basic functionality of DTs [15], the current state of the AAS is only a first part of the solution. The discovery and the definition of how operations are provided and described by standardized interfaces is ongoing work for the AAS.…”

Section: Related Workmentioning

confidence: 99%

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Generic Digital Twin Architecture for Industrial Energy Systems

et al. 2020

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Digital Twins have been in the focus of research in recent years, trying to achieve the vision of Industry 4.0. In the domain of industrial energy systems, they are applied to facilitate a flexible and optimized operation. With the help of Digital Twins, the industry can participate even stronger in the ongoing renewable energy transition. Current Digital Twin implementations are often application-specific solutions without general architectural concepts and their structures and namings differ, although the basic concepts are quite similar. For this reason, we analyzed concepts, architectures, and frameworks for Digital Twins in the literature to develop a technology-independent Generic Digital Twin Architecture (GDTA), which is aligned with the information technology layers of the Reference Architecture Model Industry 4.0 (RAMI4.0). This alignment facilitates a common naming and understanding of the proposed architectural structure. A proof-of-concept shows the application of Semantic Web technologies for instantiating the proposed GDTA for a use case of a Packed-Bed Thermal Energy Storage (PBTES).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Generic Digital Twin Architecture for Industrial Energy Systems

et al. 2020

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“…Cognitive computing is machines' ability to mimic the human capacity to sense, think, and make optimal decisions in a given situation [5]. While the path reaching fully cognitive systems is still in its early stage, there are several application areas where the technology has already been implemented in many applications such as chatbots by the service sector to provide optimal responses to customer feedback [6].…”

Section: Introductionmentioning

confidence: 99%

An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management

et al. 2021

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In the digital transformation era in the Architecture, Engineering, and Construction (AEC) industry, Cognitive Digital Twins (CDT) are introduced as part of the next level of process automation and control towards Construction 4.0. CDT incorporates cognitive abilities to detect complex and unpredictable actions and reason about dynamic process optimization strategies to support decision-making in building lifecycle management (BLM). Nevertheless, there is a lack of understanding of the real impact of CDT integration, Machine Learning (ML), Cyber-Physical Systems (CPS), Big Data, Artificial Intelligence (AI), and Internet of Things (IoT), all connected to self-learning hybrid models with proactive cognitive capabilities for different phases of the building asset lifecycle. This study investigates the applicability, interoperability, and integrability of an adapted model of CDT for BLM to identify and close this gap. Surveys of industry experts were performed focusing on life cycle-centric applicability, interoperability, and the CDT model’s integration in practice besides decision support capabilities and AEC industry insights. The evaluation of the adapted model of CDT model support approaching the development of CDT for process optimization and decision-making purposes, as well as integrability enablers confirms progression towards Construction 4.0.

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Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery

Albayrak¹,

Unal²

2021

Advances in Intelligent Systems and Computing

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COGNITWIN – Hybrid and Cognitive Digital Twins for the Process Industry

Cited by 69 publications

References 9 publications

Generic Digital Twin Architecture for Industrial Energy Systems

Generic Digital Twin Architecture for Industrial Energy Systems

An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management

Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery

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