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国外文献 > 1.轴承 > 2. 轴承类型与部件 > 滚动轴承

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标 题： A Combined Temporal Convolutional Network and Gated Recurrent Unit for the Remaining Useful Life Prediction of Rolling Element Bearings
用于滚动轴承剩余使用寿命预测的联合时域卷积网络与门控循环单元
作 者： Hong Tan
Yajun Shang
Hongjiao Luo
Tian Ran Lin
关键词： Temporal convolutional network  [时域卷积网络]
RUL prediction  [ RUL预测]
Rolling element bearing  [ 滚动轴承]
GRU  [ GRU]
出版年： 2024
文集名： Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023), Volume 2
联合国大会、DAMAS、IncoME和TEPEN会议联合会议（UNIfied 2023）会议纪要，第二卷
会议名： 2023 International Conference on Damage Assessment of Structures (DAMAS 2023), 2023 International Conference on Maintenance Engineering (IncoME 2023), 2023 Conference of the Efficiency and Performance Engineering Network (TEPEN 2023)
2023年结构损伤评估国际会议（DAMAS 2023）、2023年维护工程国际会议（IncoME 2023）、2023年效率与性能工程网络会议（TEPEN 2023）
届 次：
摘 要： An accurate prediction of bearing remaining useful life (RUL) can largely reduce the operation and maintenance cost of a machine. This then motivates the work presented in this study where a bearing RUL prediction technique based on a combined temporal convolutional network (TCN) and gated recurrent unit (GRU) is developed. The TCN is used to extract the detailed spatial features from the raw data whilst the GRU is employed to learn long-term dependencies between features simultaneously to enhance the RUL prediction accuracy of rolling element bearings. The validity of the proposed technique is examined using the published data from the IEEE-PHM2012 data challenge. It is found that the proposed technique can yield a satisfying lifecycle degradation trend using the end-to-end bearing degradation data. The efficacy of the proposed technique is also examined in the study by comparing the prediction result with those obtained using existing RUL prediction techniques. It is shown that the proposed technique has a better performance in bearing RUL prediction than other techniques.

准确预测轴承剩余使用寿命（RUL）可大幅降低设备的运行和维护成本。因此，本研究提出了一种基于时间卷积网络（TCN）与门控循环单元（GRU）相结合的轴承RUL预测方法。其中，TCN用于从原始数据中提取详细的时空特征，而GRU则用于同时学习特征间的长期依赖关系，从而提升滚动元件轴承的寿命预测精度。该方法的有效性通过IEEE-PHM2012数据挑战赛中的公开数据进行了验证。结果表明，该方法能够利用端到端的轴承退化数据获得令人满意的生命周期退化趋势。此外，研究还通过将预测结果与现有RUL预测技术的结果进行对比，评估了该方法的性能。结果显示，所提出的预测方法在轴承RUL预测方面优于其他现有技术。
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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