QUILT

Abstract

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, this paper contributes with 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. To validate our methodology, in this paper, we focus on building a digital twin model of a Fused-Deposition Modeling (FDM) based Cartesian additive manufacturing system. The proposed methodology achieves 83.09% accuracy in anomaly localization. To the best of our knowledge, this is the first work demonstrating the possibility of modeling and maintaining a living digital twin of a manufacturing system by extracting information from the side-channels using low-end IoT sensors.