METHODS AND TECHNOLOGIES FOR THE DEVELOPMENT OF DIGITAL TWINS FOR GUARANTEE-CAPABLE SYSTEMS OF THE INDUSTRIAL INTERNET OF THINGS
DOI:
https://doi.org/10.26906/SUNZ.2022.4.127Keywords:
digital twins, digital twin industries, industry 40, IoT, predictive maintanenceAbstract
With the development of industries through industrial revolution 4.0, active digitization of aspects of life from transport to commerce, the availability of technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, there is a growing demand for Digital Twings (DT), that can improve safety and reduce costs by collecting data, their analysis on models of real objects to make effective decisions in real time. The purpose of this article is to review the concept of DT, analyze the key domains together with examples of their use, features, problems, limitations, and benefits, and formalize common problems and limitations in DT. The paper reviewed the concept of DTs, which help in making decisions in real time to increase the efficiency of work, as well as to mitigate or prevent unexpected events during the life cycle of a real object. There are three main modeling approaches: fundamental modeling, data-driven modeling, and hybrid modeling. Another view of DT is the use of hierarchy - duplicates of components, assets, systems and processes. DTs can represent simple sensors and pumps, or DTs can be like systems and combine and model several production subsystems. The concept of DT can be applied to almost all fields of activity, but this article examines the most common industries that can be considered the main ones or that have influenced the development of Digital Twins. The analysis of key industries for the use of DT was carried out, the features and problems of application in each of the considered domains were determined. It was formulated what are the common challenges, advantages and features of digital twins.Downloads
References
Jamwal A., Agrawal R., Sharma M., Giallanza A (2021), Industry 4.0 technologies for manufacturing sustainability: a systematic review and future research directions, Applied Sciences 2021, 11(12), 5725.
da Silva Mendonca R., de Oliveira Lins S., de Bessa I. V., de CarvalhoAyres F. A. Jr., de Medeiros R. L. P., de Lucena V. F. Jr (2022), Digital Twin Applications: A Survey of Recent Advances and Challenges, Processes 2022, 10, 744.
Concetta Semeraro, Mario Lezoche, Hervé Panetto, Michele Dassisti (2021), Digital twin paradigm: A systematic literature review, Computers in Industry, Elsevier, 2021, 130, pp.103469.
Why IoT is the Backbone for Digital Twin (2020), available at: https://www.ptc.com/en/blogs/corporate/iot-digital-twin (accessed August, 2022).
Top 10 Digital Twin Companies Impacting Industry 4.0 Innovations in 2021 (2022), available at: https://www.emergenresearch.com/blog/top-10-digital-twin-companies-impacting-industry-4-0-innovations-in-2021 (accessed August, 2022).
Singh M., Fuenmayor E., Hinchy E. P., Qiao Y., Murray N., Devine D (2021), Digital Twin: Origin to Future, Applied System Innovation 2021, 4, 36.
Kite-Powell J, Using Digital Twins And Drones To Capture Physical Environments (2021), available at: https://www.forbes.com/sites/jenniferhicks/2021/12/28/using-digital-twins-and-drones-to-capture-physicalenironments/? sh=31ca46e2556e (accessed August, 2022).
Asia/Pacific* Leads the Shift to Digital-First with 1 in 3 Companies Generating More Than 30% Revenues from Digital Products and Services By 2023 , IDC Predicts (2021), Available at: https://www.idc.com/getdoc.jsp?containerId=prAP48347921 (accessed August, 2022).
Dozortsev Victor, Digital twins in industry: genesis, composition, terminology, technologies, platforms, prospects. Part 2. Key technologies of digital twins. Types of a physical object modeling (2020). Automation in Industry, 2020, No. 11, 3-10.
Qian C., Liu X., Ripley C., Qian M., Liang F., Yu W, Digital Twin – Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions (2022), Future Internet 2022, 14, 64.
Fuller A., Fan Z., Day C., Barlow C, Digital Twin: Enabling Technologies, Challenges and Open Research (2020), IEEE Access 2020, 8, 108952–108971.
Singh M., Srivastava R., Fuenmayor E., Kuts V., Qiao Y., Murray N., Devine D (2022), Applications of Digital Twin across Industries: A Review, Appl. Sci. 2022, 12, 5727.
Botín-Sanabria D.M., Mihaita A.-S., Peimbert-García R.E., Ramírez-Moreno M.A., Ramírez-Mendoza R.A., Lozoya-Santos J.d.J, Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022), Remote Sensing 2022, 14, 1335.
Autiosalo Juuso, Discovering the Digital Twin Web - From singular applications to a scalable network (2021), available at: https://aaltodoc.aalto.fi/handle/123456789/111416 (accessed August, 2022).
Shahriar M. (2020), Towards a Cyber-Physical Manufacturing Cloud through Operable Digital Twins and Virtual Production Lines, available at: https://scholarworks.uark.edu/etd/3739 (accessed August, 2022).
Vyacheslav Kharchenko, Olga Morozova. Digital Twin for Logistics System of the Manufacturing Enterprise Using Industrial IoT (2019), vol. 45, no. 1 (2019): pp-zz.
Stojanovic L., Uslander T., Volz F., Weibenbacher C., Muller J., Jacoby M., Bischoff T, Methodology and Tools for Digital Twin Management – The FA3ST Approach (2021), IoT 2021, 2, 717–740.
He F., Ong S. K., Nee A. Y. C, An Integrated Mobile Augmented Reality Digital Twin Monitoring System (2021), Computers 2021, 10, 99.
Kampczyk A., Dybeł K, The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions (2021), Sensors 2021, 21, 5757.
Z. Lv D. Chen and M. S. Hossain, Traffic Safety Detection System by Digital Twins and Virtual Reality Technology (2022), IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2022, pp. 1-6.
Hazal Şimşek, Top 5 Use Cases of Digital Twin in Automotive Industry in 2022 (2022), available at: https://research.aimultiple.com/digital-twin-automotive/ (accessed August, 2022).
Hazal Şimşek, Best Digital Twin Applications & Use Cases in Healthcare in 2022 (2022), available at: https://research.aimultiple.com/digital-twin-healthcare/ (accessed August, 2022),
Y. Liu et al., A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin (2019), in IEEE Access, vol. 7, pp. 49088-49101, 2019.
Cory Kays & team (Aurora Flight Sciences), David Knezevic & Phuong Huynh (Akselos), Michael Kapteyn (MIT PhD student), Jacob Pretorius (Jessara Group), Development of a Predictive Digital Twin (2021), available at: https://kiwi.oden.utexas.edu/research/digital-twin (accessed August, 2022).
Shaun Waterman, Air Force Goes All in on Digital Twinning—for Bombs As Well As Planes (2021), available at: https://www.airforcemag.com/air-force-goes-all-in-on-digital-twinning-for-bombs-as-well-as-planes/ (accessed August, 2022).
Schrotter G., Hürzeler C, The Digital Twin of the City of Zurich for Urban Planning (2020), PFG 88, 99–112 (2020).
Tianhu Deng, Keren Zhang, Zuo-Jun (Max) Shen, A systematic review of a digital twin city: A new pattern of urban governance toward smart cities, Journal of Management Science and Engineering (2021), Volume 6, Issue 2, 2021, Pages 125-134, ISSN 2096-2320.
David N. Ford, Charles M. Wolf, Smart Cities with Digital Twin Systems for Disaster Management (2020), Journal of Management in Engineering Vol. 36, Issue 4 (July 2020).
Shahat Ehab, Chang T. Hyun, Chunho Yeom, City Digital Twin Potentials: A Review and Research Agenda (2021), Sustainability 13, no. 6: 3386.
Joe David, development of a digital twin of a flexible manufacturing system for assisted learning (2018), Available at: https://www.researchgate.net/publication/335234337_Development_of_a_digital_twin_of_a_flexible_manufacturing_system_for_assisted_learning (accessed August, 2022).
Christian Stary, Claudia Kaar, Sabrina Oppl, Dominik Schuhmann, Johannes Kepler University Linz, Tangibles and Digital Twins: Toward Meaningful Learning Support in CyberPhysical System Development (2022), ISBN: 978-1-912532-28-5.
Yitmen I., Alizadehsalehi S., Akıner ˙I., Akıner M. E, An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management (2021), Applied Sciences 2021, 11, 4276.
Salem T., Dragomir M, Options for and Challenges of Employing Digital Twins in Construction Management (2022), Applied Sciences 2022, 12, 2928.
What is industry 4.0? (2016), available at: http://www.industriall-union.org/industry-40-the-industrial-revolution-happeningnow/ (accessed August, 2022).
Aheleroff S., Xu X., Zhong R. Y., Lu Y, Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model (2021), Advanced Engineering Informatics 2021, 47(2), 101225.
Padovano A., Longo F., Nicoletti L., Mirabelli G, A Digital Twin based Service Oriented Application for a 4.0 Knowledge Navigation in the Smart Factory (2018), IFAC-PapersOnLine 2018, 51(11), 631–636.
Al-Ali A. R., Gupta R., Batool T. Z., Landolsi T., Aloul F., Al Nabulsi A, Digital Twin Conceptual Model within the Context of Internet of Things (2020), Future Internet 2020, 12, 163.
Start Innovating with Digital Twins Technology, Available at: https://www.perforce.com/p/resources/vcs/digital-twinstechnology (accessed August, 2022).
Hou Lei, Shaoze Wu, Guomin Zhang, Yongtao Tan, and Xiangyu Wang, Literature Review of Digital Twins Applications in Construction Workforce Safety (2020), Applied Sciences 11, no. 1: 339.
Kaewunruen Sakdirat, Sresakoolchai Jessada, Lin Yi-hsuan, Digital twins for managing railway maintenance and resilience (2021), Open Research Europe. 1. 91. 10.12688/openreseurope.13806.1.
Dirnfeld Ruth, Digital Twins in Railways (2022), 10.13140/RG.2.2.32690.68804.
Kampczyk Arkadiusz, Dybeł Katarzyna, The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions (2021), Sensors. 21(17). 5757. 10.3390/s21175757.
Katharina Rombach, Towards a Data-driven Operational Digital Twin for Railway Wheels (2022) available at: https://youtu.be/5igWA9wuDdw (accessed August, 2022).
