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A digital twin is the virtual replica of a physical system. Digital twins are useful because they provide models and data for design, production, operation, diagnostics, and prognostics of machines and products. Traditionally, building a digital twin requires many built-in sensors to monitor various physical phenomena associated with cyber-physical systems such as vibration, energy consumption, etc. However, many legacy manufacturing systems do not have multi-physics sensors built-in by default. Moreover, it might not be feasible to intrusively place sensors in these systems after they are manufactured. To bring the advantages of digitalization to legacy manufacturing systems, in this chapter, we present an Internet-of-Things (IoT) based methodology to build digital twins using an indirect medium such as side-channels, which can localize anomalous faults and infer the quality of the products being manufactured while keeping itself up-to-date. We achieve this by exploring and utilizing the side-channels (emissions such as acoustics, power, magnetic, etc.) of the system that unintentionally reveal the cyber and physical state of the system.
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With high-end sensors as theirs, our methodology may achieve higher accuracy in anomaly detection along with the capability of keeping the digital twin most up-to-date at the cost of more computational and resource requirements, which may not be feasible for an IoT paradigm.
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- IoT-Enabled Living Digital Twin Modeling
Sujit Rokka Chhetri
Mohammad Abdullah Al Faruque
- Chapter 8