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国外文献 > 1.轴承 > 5. 监测与诊断 > 寿命与维护

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标 题： A comparison study for bearing remaining useful life prediction by using standard stochastic approach and digital twin
基于标准随机方法与数字孪生技术的轴承剩余使用寿命预测比较研究
作 者： Jie Liu
Jorn Vatn
Viggo Gabriel Borg Pedersen
Shen Yin
Bahareh Tajiani
关键词： Remaining useful life  [剩余使用寿命]
Digital twin  [ 数字孪生]
Stochastic approach  [ 随机方法]
Predictive maintenance  [ 预测性维护]
Bearing experiment  [ 轴承实验]
出版年： 2023
期刊名： International Journal of Reliability and Safety
可靠性与安全国际期刊
年卷期： 2023, vol.17, no.2
摘 要： Remaining useful life (RUL) prediction is important for research of maintenance. It is common to use stochastic approaches to predict RUL of components. On the other hand, there is a digital twin model developed by MATLAB for bearing's real-time RUL prediction. To have a better understanding of the advantages and disadvantages of these models, an experiment was designed and implemented to get real degradation data of bearings for model testing. Two stochastic approaches are selected which are Wiener process and Geometric Brownian Motion. The purpose of the paper is to compare the models for RUL prediction with standard stochastic approaches and digital twin through real degradation data in order to compare them. Finally, the MATLAB digital twin model outperforms stochastic approaches in the early phases of prediction while remaining comparable in the latter stages. The paper could be used as a reference for further RUL prediction research.

剩余使用寿命（RUL）预测对于维护研究具有重要意义。通常采用随机方法来预测部件的RUL。另一方面，MATLAB已开发出用于轴承实时RUL预测的数字孪生模型。为了更好地理解这些模型的优缺点，本文设计并实施了一项实验，以获取轴承的真实劣化数据进行模型测试。选取了两种随机方法：维纳过程和几何布朗运动。本文旨在通过真实劣化数据，对RUL预测模型与标准随机方法及数字孪生模型进行比较。最终结果表明，在预测的早期阶段，MATLAB数字孪生模型优于随机方法，而在后期阶段则表现相当。本文可作为未来RUL预测研究的参考依据。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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