scholarly journals Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case

Machines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 193
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
Kenneth N. Brown ◽  
Donna O’Shea ◽  
Conor McCarthy ◽  
...  
Keyword(s):  
Decision Making ◽  
Conceptual Framework ◽  
Manufacturing Systems ◽  
Data Representation ◽  
Distributed Manufacturing ◽  
Smart Transportation ◽  
Digital Twins ◽  
Physical Assets ◽  
Operational Data ◽  
Distributed Manufacturing Systems

Digital twins (DTs) is a promising technology in the revolution of the industry and essential for Industry 4.0. DTs play a vital role in improving distributed manufacturing, providing up-to-date operational data representation of physical assets, supporting decision-making, and avoiding the potential risks in distributed manufacturing systems. Furthermore, DTs need to collaborate within distributed manufacturing systems to predict the risks and reach consensus-based decision-making. However, DTs collaboration suffers from single failure due to attack and connection in a centralized manner, data interoperability, authentication, and scalability. To overcome the above challenges, we have discussed the major high-level requirements for the DTs collaboration. Then, we have proposed a conceptual framework to fulfill the DTs collaboration requirements by using the combination of blockchain, predictive analysis techniques, and DTs technologies. The proposed framework aims to empower more intelligence DTs based on blockchain technology. In particular, we propose a concrete ledger-based collaborative DTs framework that focuses on real-time operational data analytics and distributed consensus algorithms. Furthermore, we describe how the conceptual framework can be applied using smart transportation system use cases, i.e., smart logistics and railway predictive maintenance. Finally, we highlighted the future direction to guide interested researchers in this interesting area.

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

scholarly journals Methodology and Tools for Digital Twin Management—The FA3ST Approach

IoT ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 717-740
Author(s):  
Ljiljana Stojanovic ◽  
Thomas Usländer ◽  
Friedrich Volz ◽  
Christian Weißenbacher ◽  
Jens Müller ◽  
...  
Keyword(s):  
Decision Making ◽  
Industrial Applications ◽  
Industrie 4.0 ◽  
Conceptual Approach ◽  
Digital Twin ◽  
Digital Representations ◽  
Lifecycle Model ◽  
Digital Twins ◽  
International Data ◽  
Physical Assets

The concept of digital twins (DT) has already been discussed some decades ago. Digital representations of physical assets are key components in industrial applications as they are the basis for decision making. What is new is the conceptual approach to consider DT as well-defined software entities themselves that follow the whole lifecycle of their physical counterparts from the engineering, operation up to the discharge, and hence, have their own type description, identity, and lifecycle. This paper elaborates on this idea and argues the need for systematic DT engineering and management. After a conceptual description of DT, the paper proposes a DT lifecycle model and presents methodologies and tools for DT management, also in the context of Industrie 4.0 concepts, such as the asset administration shell (AAS), the international data spaces (IDS), and IEC standards (such as OPC UA and AML). As a tool example for the support of DT engineering and management, the Fraunhofer-advanced AAS tools for digital twins (FA3ST) are presented in more detail.

A Standards and Technology Roadmap for Scalable Distributed Manufacturing Systems

2018 ◽  
Author(s):  
Thomas Hedberg ◽  
Moneer Helu ◽  
Timothy Sprock
Keyword(s):  
Manufacturing Systems ◽  
Dynamic Decision Making ◽  
Smart Manufacturing ◽  
Distributed Manufacturing ◽  
Physical Systems ◽  
Technology Roadmap ◽  
Industrial Internet ◽  
Manufacturing Resources ◽  
Industrial Standards ◽  
Distributed Manufacturing Systems

The increasing decentralization of manufacturing has contributed to the growing interest in scalable distributed manufacturing systems (DMSs). The emerging body of work from smart manufacturing, Industrie 4.0, Industrial Internet of Things (IIoT), and cyber-physical systems can enable the continued development of scalable DMS, particularly through the digital thread. However, significant challenges exist in understanding how to apply the digital thread most appropriately for scalable DMS. This paper describes these major challenges and provides a standards and technology roadmap developed from the digital thread viewpoint and consensus-built industrial standards to realize scalable DMS. The goal of this roadmap is to guide research that enables manufacturers to take advantage of opportunities provided by scalable DMS, including improved agility, flexibility, traceability, dynamic decision making, and utilization of manufacturing resources.

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