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Towards a Digital Twin of a Robot Workcell to Support Prognostics and Health Management

Published

Author(s)

Deogratias Kibira, Brian A. Weiss

Abstract

Current maintenance research often includes modeling the equipment degradation to determine degradation and in particular, when any degradation will exceed a specified threshold. Such models can provide critical intelligence to determine an impending failure and promote the timely scheduling of maintenance, yet, the models require equipment data. While healthy state data can be readily captured from a system, degraded or failure state data is more difficult to acquire because equipment are normally operating in a healthy state. A digital twin can model the degradation process and generate data to predict a system's future health state. This paper presents a procedure for building a digital twin to model and generate data of a robot workcell during operation. Future work will focus on the incorporation of robot degradations and generate data that can be input into analytics and the results used to predict future states of the robot and support decision-making.
Proceedings Title
Proceedings of the 2022 Winter Simulation Conference
Conference Dates
December 11-14, 2022
Conference Location
Marina Bay Sands, SG
Conference Title
Winter Simulation Conference 2022

Keywords

prognostics and health management (PHM), digital twin, degradation data, robot workcell, physical modeling

Citation

Kibira, D. and Weiss, B. (2022), Towards a Digital Twin of a Robot Workcell to Support Prognostics and Health Management, Proceedings of the 2022 Winter Simulation Conference, Marina Bay Sands, SG, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934772 (Accessed June 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created December 14, 2022, Updated January 19, 2023