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 feature fusion-based prognostics approach for rolling element bearings
基于特征融合的滚动轴承预测方法 |
| 作 者： |
Akpudo, Ugochukwu Ejike
Hur, Jang-Wook |
| 关键词： |
Long short-term memory [长短期记忆]
Kernel principal component analysis [ 主成分分析（PCA）]
Bearing degradation assessment [ 轴承劣化评估]
Feature fusion [ 特征融合]
Remaining useful life [ 剩余使用寿命]
KPCA-DLSTM [ KPCA-DLSTM] |
| 出版年： |
2020 |
| 期刊名： |
Journal of Mechanical Science and Technology
机械科学与技术杂志 |
| 年卷期： |
2020, vol.34, no.10 |
| 摘 要： |
The emergence of prognostics and health management as a condition-based maintenance approach has greatly improved productivity, maintainability, and most essentially, reliability of systems. Invariably, a rolling-element bearing (REB) is the heart of rotating components; however, its failure can have daunting effects ranging from costly unexpected breakdown to catastrophic life-threatening situations. Consequently, the need for accurate condition monitoring and prognostics of REBs cannot be overemphasized. In view of achieving a more comprehensive condition assessment for prognostics of REBs, this study proposes a kernel principal component analysis (KPCA) feature fusion technique for degradation assessment and a deep learning model for prognostics. The deep learning method-deep long short-term memory (DLSTM) has shown an evident comparative advantage over the basic LSTM model and standard recurrent neural networks for time-series forecasting. Subsequently, the proposed prognostics model-KPCA-DLSTM performance was validated with a run-to-failure experiment on REBs and evaluated for accuracy against other prognostics methods reported in other works of literature using standard performance metrics. The proposed method was also used for REB remaining useful life (RUL) prediction and the results show that the KPCA-DLSTM does not only reflect a more monotonic bearing degradation trend but also yields better prognostics results.
预测性维护和健康管理作为基于状态的维护方法，极大地提高了系统的生产效率、可维护性以及最重要的可靠性。滚动轴承（REB）始终是旋转部件的核心； 然而，其失效可能带来令人担忧的后果，从昂贵的意外故障到危及生命的严重情况。因此，对旋转设备（REB）进行准确的状态监测和预测显得尤为重要。为实现更全面的REB状态评估以支持预测，本研究提出了一种基于核主成分分析（KPCA）的特征融合技术用于劣化评估，并结合深度学习模型进行预测。在时间序列预测方面，深度学习方法——深度长短期记忆网络（DLSTM）相较于传统的LSTM模型和标准循环神经网络展现出明显优势。随后，该预测模型KPCA-DLSTM通过REB的“运行至失效”实验进行了性能验证，并采用标准性能指标与其他文献中报道的预测方法进行对比评估。此外，该方法还被用于预测REB的剩余使用寿命（RUL），结果表明，KPCA-DLSTM不仅能够更准确地反映轴承劣化趋势的单调性，而且在预测精度上也优于其他方法。 |
| 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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