While there has been a recent growth of interest in the Digital Twin, a variety of definitions employed across industry and academia remain. There is a need to consolidate research such to maintain a common understanding of the topic and ensure future research efforts are to be based on solid foundations. Through a systematic literature review and a thematic analysis of 92 Digital Twin publications from the last ten years, this paper provides a characterisation of the Digital Twin, identification of gaps in knowledge, and required areas of future research. In characterising the Digital Twin, the state of the concept, key terminology, and associated processes are identified, discussed, and consolidated to produce 13 characteristics (Physical Entity/Twin; Virtual Entity/Twin; Physical Environment; Virtual Environment; State; Realisation; Metrology; Twinning; Twinning Rate; Physical-to-Virtual Connection/Twinning; Virtual-to-Physical Connection/Twinning; Physical Processes; and Virtual Processes) and a complete framework of the Digital Twin and its process of operation. Following this characterisation, seven knowledge gaps and topics for future research focus are identified: Perceived Benefits; Digital Twin across the Product Life-Cycle; Use-Cases; Technical Implementations; Levels of Fidelity; Data Ownership; and Integration between Virtual Entities; each of which are required to realise the Digital Twin.

https://research-information.bris.ac.uk/files/229965388/Characterising_The_Digital_Twin.pdf

Characterising the Digital Twin: A systematic literature review

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

Digital Twin: Enabling Technologies, Challenges and Open Research

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

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Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Review of digital twin about concepts, technologies, and industrial applications

Enabling technologies and tools for digital twin

With the rapid development of virtual technology and data acquisition technology, digital twin (DT) technology was proposed and gradually become one of the key research directions of intelligent manufacturing. However, the research of DT for product life cycle management is still in the theoretical stage, the application framework and application methods are not clear, and the lack of referable application cases is also a problem. In this paper, the related research and application of DT technology are systematically studied. Then the concept and characteristics of DT are interpreted from both broad sense and narrow sense. On this basis, an application framework of DT for product lifecycle management is proposed. In physical space, the total-elements information perception technology of production is discussed in detail. In the information processing layer, three main function modules, including data storage, data processing and data mapping, are constructed. In virtual space, this paper describes the implementation process of full parametric virtual modeling and the construction idea for DT application subsystems. At last, a DT case of a welding production line is built and studied. Meanwhile, the implementation scheme, application process and effect of this case are detail described to provide reference for enterprises.

An application framework of digital twin and its case study

An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation

Purpose – Making decisions on production and maintenance separately, as is often done in practice and research literature, may not result in overall optimization. This paper aims to propose a joint method that better integrates production planning and maintenance at the tactical level. The potential of improving the performance of the classic planning method is also explored. Design/methodology/approach – An integrated production planning and maintenance model is proposed. The production capacity losses resulted from both preventive and corrective maintenance activities are considered. Meanwhile, the reliability deterioration of the machine is considered to be operation dependent. An iterative approach is presented to find a solution for the nonlinear model through iteratively solving a sequence of mixed integer linear programming instances, accompanied by modification of some parameters prior to each iteration. Computational experiments are conducted to evaluate the performance of the proposed method com...

Integrating production planning and maintenance: an iterative method

A spreader is an important part of a crane used on wharfs for loading and unloading operations. Because of the spreader’s complex structure, poor operation environment, frequent collision accidents, etc., breakdowns and faults often occur, and the causes for the faults are complex. Based on the historical fault data of the spreaders accumulated during their online service for 13 years, by determining top events and boundary conditions using the induction and deduction methods, a complete spreader fault tree is built with three layers of fault phenomena, fault classification, and fault causes. Then, based on the fault tree, a Bayesian network for the spreader fault diagnosis is constructed by establishing the transformation algorithm from the fault tree to the Bayesian network. The junction tree method is used for accurate inference of spreader faults based on the Bayesian network. Finally, a spreader fault diagnosis system is developed, and a case verification is carried out. The system would be of great help to crane operation engineers in fault diagnosis, and it effectively uses historical fault data to support subsequent maintenance.

Yu Zheng

Papers

An application framework of digital twin and its case study

An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation

Integrating production planning and maintenance: an iterative method

Fault diagnosis system of bridge crane equipment based on fault tree and Bayesian network

Multiscale similarity ensemble framework for remaining useful life prediction