期刊
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
卷 32, 期 11, 页码 1067-1080出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/0951192X.2019.1686173
关键词
Manufacturing; predictive maintenance; simulation; RUL prediction; physics-based model
类别
资金
- European Union's Horizon 2020 research and innovation programme [767287]
- H2020 Societal Challenges Programme [767287] Funding Source: H2020 Societal Challenges Programme
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据