基于动态加权卷积长短时记忆网络的滚动轴承剩余寿命预测方法

蒋全胜1,许伟洋1,朱俊俊1,沈晔湖1,徐丰羽2

振动与冲击 ›› 2022, Vol. 41 ›› Issue (17) : 282-291.

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (17) : 282-291.
论文

基于动态加权卷积长短时记忆网络的滚动轴承剩余寿命预测方法

  • 蒋全胜1,许伟洋1,朱俊俊1,沈晔湖1,徐丰羽2
作者信息 +

Residual life prediction method of rolling bearing based on DW-CNN-LSTM networks

  • JIANG Quansheng1, XU Weiyang1, ZHU Junjun1, SHEN Yehu1, XU Fengyu2
Author information +
文章历史 +

摘要

现有数据驱动方法在滚动轴承剩余使用寿命预测中,因不能有效提取对轴承退化过程敏感的特征信息而导致预测精度不足。为此提出一种基于动态加权卷积长短时记忆网络(DW-CNN-LSTM)的滚动轴承剩余寿命预测方法。首先对滚动轴承振动信号进行小波包分解,将获得的小波包系数矩阵通过可训练参数动态加权层进行动态加权,来实现对轴承退化的表征信息进行有效筛选,以增强轴承振动特征学习能力;然后利用卷积神经网络的自适应挖掘数据深层特征能力,从动态加权后的小波包系数矩阵中提取对轴承退化过程敏感的特征集;最后借助长短时记忆网络(LSTM)预测时间信息序列的优势,由双层LSTM进一步提取其高维退化特征,来提高滚动轴承剩余寿命预测精度。对XJTU-SY轴承数据和IMS轴承数据的实验结果表明,所提DW-CNN-LSTM方法相比于经典的长短时记忆网络方法,其均方根误差指标平均降低了61.08%,预测准确度平均提高了9.95%,模型训练时间平均减少了44.14%,获得了较满意的寿命预测精度和鲁棒性。
关键词 :滚动轴承;剩余寿命预测;卷积长短时记忆网络;小波包分解;动态加权

Abstract

In the prediction of the remaining useful life of rolling bearings, data-driven methods cannot effectively extract the feature information sensitive to the degradation process of bearings, resulting in insufficient prediction accuracy. Therefore, a RUL prediction method of rolling bearing based on dynamic weighted convolution long short-term memory network (DW-CNN-LSTM) is proposed. Firstly, the rolling bearing vibration signal is decomposed by wavelet packet, and then the decomposed wavelet packet coefficient matrix is dynamically weighted by the dynamic weighting layer of trainable parameters to effectively screen the information that can characterize the bearing degradation, so as to enhance the feature learning ability of the model to the bearing vibration signal. Then, using the ability of convolutional neural network to adaptively learn and mine the deep features of data, the feature set sensitive to the bearing degradation process is extracted from the dynamically weighted wavelet packet coefficient matrix. Finally, with the advantage of long short-term memory (LSTM) to predict time information series, the high-dimensional degradation characteristics are further extracted through the double-layer LSTM to improve the prediction accuracy of rolling bearing RUL. The experimental results on XJTU-SY bearing dataset and IMS bearing dataset show that the proposed DW-CNN-LSTM method is better than the classical LSTM method in bearing life prediction accuracy, especially the root mean square error index of the proposed method is reduced by 61.08%, the prediction accuracy is improved by 9.95%, and the average training time of the model was reduced by 44.14%.
Key words:Rolling bearing; Prediction of the remaining useful life; Convolution long short-time memory network; Wavelet packet decomposition; Dynamic weighting

关键词

滚动轴承 / 剩余寿命预测 / 卷积长短时记忆网络 / 小波包分解 / 动态加权

Key words

Rolling bearing / Prediction of the remaining useful life / Convolution long short-time memory network / Wavelet packet decomposition / Dynamic weighting

引用本文

导出引用
蒋全胜1,许伟洋1,朱俊俊1,沈晔湖1,徐丰羽2. 基于动态加权卷积长短时记忆网络的滚动轴承剩余寿命预测方法[J]. 振动与冲击, 2022, 41(17): 282-291
JIANG Quansheng1, XU Weiyang1, ZHU Junjun1, SHEN Yehu1, XU Fengyu2. Residual life prediction method of rolling bearing based on DW-CNN-LSTM networks[J]. Journal of Vibration and Shock, 2022, 41(17): 282-291

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