Elaris Computing Nexus
Digital Twin-Based Multimodal Condition Monitoring of Asynchronous Motors
Elaris Computing Nexus
Received On : 18 June 2026
Revised On : 30 July 2026
Accepted On : 16 August 2026
Published On : 28 August 2026
Volume 02, 2026
Pages : 156-166
Abstract
The objective of this work is to propose and demonstrate a multimodal digital twin framework for the condition monitoring of the robotic automation cell in the edge-to-cloud. This system is comprised of a Robotic platform of 3 conveyors, CoppeliaSim model and an edge monitoring module on a Nano 33 BLE Sense development board Revision II. It was tested on the induction motor at 1500, 1200 and 900 RPM (revolutions per minute) with no-load and with no-disturbance. The tri-axial vibration and acoustic data were captured at 1 kHz and the temperature was experimented at 1 Hz for 60 s at each speed. Vibration distributions, acoustic Mel spectrograms and temperature trends were investigated. The results reveal multimodal fluctuations depending on the speed and could serve as a reference for future anomaly detection and predictive maintenance in robotic production systems based on AI.
Keywords
Digital Twin, Edge Computing, Cloud Computing, Robotic Automation, Condition Monitoring, Asynchronous Motor, Vibration Analysis, Acoustic Monitoring, Predictive Maintenance, CoppeliaSim.
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Cite this Article
Jiang Lingge, “Digital Twin-Based Multimodal Condition Monitoring of Asynchronous Motors”, Elaris Computing Nexus, pp. 156-166, 2026, doi: 10.65148/ECN/2026013.
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© 2026 Jiang Lingge. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.