基于深度学习的轴承数字孪生建模及剩余寿命预测仿真研究

Jieting Huang, Tan Li, Weining Song, Zhiming Zheng
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引用次数: 0

摘要

现代数字孪生模型通常基于制造全生命周期的海量实时数据构建,实现物理系统的实时虚拟表示,并应用于预测仿真,为后续运行提供优化建议和故障预警。建立了四种基于深度学习的轴承剩余寿命预测方法,包括经典的CNN和RNN, LSTM和CNN-LSTM,用于构建轴承数字孪生模型。根据给定的数据集和评价指标,建立了真实滚动轴承数字孪生模型并进行了预测仿真。对仿真结果进行了分析,得出了轴承剩余寿命预测的最佳性能模型。提出了基于深度学习的数字孪生建模与仿真的未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Bearing Digital Twin Modeling and Residual Life Predictive Simulation Based on Deep Learning
Modern Digital Twin models are normally built based on massive live data from the manufacturing life-cycle to realize the real-time virtual representation of the physical system and applied in predictive simulation for optimization suggestions and fault warnings for the subsequent operation. Four residual life prediction methods based on deep learning are established to build Bearing Digital Twin models, including the classical CNN and RNN, as well as the LSTM and CNN-LSTM. The real rolling bearing digital twin models are setup and predictive simulated based on the given datasets and evaluation indicators. The simulation results are analyzed and the best performance models for Bearing residual life prediction are stated as a conclusion. Some future research points on deep learning based digital twin modeling and simulation are proposed.
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