The use of Digital Twin for predictive maintenance in manufacturing
By P. Aivaliotis, K. Georgoulias, George Chryssolouris. International journal of computer integrated manufacturing (Print), 2019
333 citations recorded by the source corpus.
ABSTRACT 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.
Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/f43e076cf5c5376106b59d39d5caaa4a7359e697
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In Industrial and Manufacturing Engineering
In Mechanical Engineering
Citations in the corpus
- Predictive maintenance using digital twins: A systematic literature review (2022, methodology): “Thirty studies make use of the Digital Monitor pattern [38-44, 46, 47, 52-57, 62, 64-70, 73-79].”
- A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, background): “, tools [116,117], bearings [118], ball screws [119], gears [15,120,121], pumps [122]) and the energy efficiency of the equipment [123,124] in order to handle the conflict between the unplanned maintenance operations and the resulting costs and productivity, particularly in SMEs, due to their limite”