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The use of Digital Twin for predictive maintenance in manufacturing

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This paper presents a methodology to calculate the Remaining Useful Life (RUL) of machinery equipment by utilising physics-based simulation models and Digital Twin concept, in order to enable predictive maintenance for manufacturing resources using Prognostics and health management (PHM) techniques. The resources and the properties of them are first modelled in a digital environment able to simulate the real machine’s behaviour. Data are gathered by machines’ controllers and external sensors to be used for the synchronous tuning of the digital models and their simulation. The outcome of the simulation is then used to assess the resource’s condition and to calculate RUL. In this way, the condition and the status of the machines can be monitored and predicted as a result from the simulation of physics-based models, without invasive techniques of common predictive maintenance solutions. A case study is presented in this paper where the proposed methodology is validated by predicting the RUL of an industrial robot.
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Keywords: Manufacturing; RUL prediction; physics-based model; predictive maintenance; simulation

Document Type: Research Article

Affiliations: Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

Publication date: November 2, 2019

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