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Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems.
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This content will become publicly available on May 19, 2026
Smart Ring based Digital Twin for Healthcare Application
Not AvailableThis work presents a digital twin framework based on smart rings. The compact design of smart rings with a composite sensor enables a digital twin as a viable option for mobile health monitoring, analysis and prediction of biomarkers over time. Built upon sensor data from a smart ring such as Photoplethysmogram (PPG), peripheral oxygen saturation (SpO2), physical motion, and others, a digital twin can provide continuous predictive insights. This framework enables the detection of anomalies in heart rate, monitoring of sleep patterns, evaluation of blood oxygen levels, and assessment of stress. Additionally, it integrates these findings into visual representations, enhancing the understanding of health outcomes.
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- Award ID(s):
- 2140729
- PAR ID:
- 10651734
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1 to 2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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