Bearing Remaining Useful Life Prediction Based on AE-BiLSTM

Jie Liu, Zian Yang, Ruijie Wang, Shanhui Liu
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引用次数: 0

Abstract

The remaining useful life (RUL) prediction of rolling bearings can avoid unreasonable maintenance and major safety accidents. Considering the non-stationary characteristics, it is difficult to utilize the deep learning-based method to directly extract degradation features from the bearing vibration signal. Therefore, in this paper, a fusion prediction model AE-BiLSTM is proposed. The AutoEncoder (AE) is used to extract degradation features from the frequency-domain signals, and BiLSTM network is used to predict the bearing RUL. The experimental verification is conducted on the FEMTO-ST bearing dataset. Experimental results illustrate that the proposed AE-BiLSTM network can accurately predict the RUL of roll bearings.
基于AE-BiLSTM的轴承剩余使用寿命预测
滚动轴承剩余使用寿命(RUL)预测可以避免不合理的维护和重大安全事故。考虑到轴承振动信号的非平稳特性,利用基于深度学习的方法直接提取轴承振动信号的退化特征是困难的。为此,本文提出了一种融合预测模型AE-BiLSTM。采用自编码器(AE)从频域信号中提取退化特征,采用BiLSTM网络预测轴承RUL。在FEMTO-ST轴承数据集上进行了实验验证。实验结果表明,所提出的AE-BiLSTM网络能够准确预测滚动轴承的RUL。
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