Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing

Abstract

Digital twin (DT) is gaining popularity due to its significant impacts on bridging the gap between the physical and cyber worlds. As reported by Grand View Research, Inc., the global market of DT is expected to reach $26.07 billion by 2025 with a Compound Annual Growth Rate of 38.2%. The growing adoption of cyber‐physical system (CPS), Internet of Things, big data analytics, and cloud computing in manufacturing sector has paved the way for low cost and systematic implementation of DT, with promising impacts on (a) product design and development, (b) machine and equipment health monitoring, and (c) product support and services. Successful implementation of DT would increase transparency, cooperation, flexibility, resilience, production speed, scalability, and manufacturing efficiency. Realisation of smart manufacturing requires collaborative and autonomous interactions between sensing, networking, and computational resources across manufacturing assets where data is gathered from physical systems is utilised for the extraction of actionable insights and provision of predictive services. In this study, a reference architecture based on deep learning, DT, and 5C‐CPS is proposed to facilitate the transformation towards smart manufacturing and Industry 4.0.

A Robust Deep Learning-Based Fault Diagnosis Method for Rotating Machinery

“…Specifically, the domain adaptation (DA) techniques have been popularly developed in the fault diagnosis field 14;15 , which assume the training and testing data share the same label space. That is consistent with the machinery health condition identification problems 16 . The domain-invariant features across different conditions are expected to be learned with the domain adaptation methods, and stronger model generalization ability can be achieved.…”

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“…Collaborative scheduling: CIM technologies should integrate 5G communication, cloud manufacturing, IoT, edge computing, big data analytics, and digital twins to increase the capability of enterprises and supply chains at five levels to cope with COVID‐19 [2, 3]. According to manufacturing business requirements, the CIM technologies should be able to connect supply chains (logistics), enterprises, factories, shop floors and equipment to realise dynamic reconfiguration of supply chains and enterprise systems.…”

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