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.

Due to the successful implementation of intelligent data-driven approaches, these methods are gaining remarkable attention in predicting the remaining useful life problems. Within this scope, transfer learning approaches are exploited to transfer the obtained knowledge from the source domain data to the target domain data. Due to the different working regimes and operating conditions, there exists a discrepancy between the data distribution of source and target domain datasets. Domain adaptation techniques are deployed to tackle the data distribution discrepancy. In most prognostic problems, it is assumed that the complete life-cycle run-to-failure information for the target domain dataset is available. However, in real practical scenarios, providing the complete life-cycle data is not straightforward. To solve this issue, this paper proposed a transfer learning approach for remaining useful life prediction using a consistency-based regularization. In the proposed deep learning framework, a consistency-based regularization term is added to the objective function to remove the negative effect of missing information in the incomplete target domain dataset. In order to further validate the effectiveness of the proposed method, a comprehensive experimental analysis has been done on two different aero-space and bearing datasets.