基于TET与DSRNet-AttBiLSTM的滚动轴承剩余使用寿命预测

周玉国1, 张金超1, 孙伊萍1, 于春风2, 周立俭1

振动与冲击 ›› 2024, Vol. 43 ›› Issue (19) : 163-173.

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (19) : 163-173.
论文

基于TET与DSRNet-AttBiLSTM的滚动轴承剩余使用寿命预测

  • 周玉国1,张金超1,孙伊萍1,于春风2,周立俭1
作者信息 +

Remaining useful life prediction of rolling bearings based on TET and DSRNet-AttBiLSTM

  • ZHOU Yuguo1, ZHANG Jinchao1, SUN Yiping1, YU Chunfeng2, ZHOU Lijian1
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文章历史 +

摘要

为了在滚动轴承剩余使用寿命(Remaining Useful Life,RUL)预测中,能够准确地提取轴承的退化特征并进行有效的RUL预测。本文提出一种基于暂态提取变换(Transient Extracting Transform,TET)与DSRNet-AttBiLSTM的滚动轴承RUL预测方法。首先,对原始振动信号分段重组后进行TET得到时频图,使用双线性插值对时频图进行降维,将降维后的时频图进行通道拼接得到轴承的时频图像化特征。其次,为了准确且有效地提取滚动轴承的退化特征,构建了包含深度可分离卷积和空间通道注意力的SConv和DConv基础模块,以此为基础建立了DSRNet来提取空间与通道两个维度下的轴承退化特征。再次,为了使双向长短时间记忆(Bidirectional Long Short-Term Memory,BiLSTM)网络在学习时更加关注具有更重要信息的输入特征,在特征输入端构建了注意力层,并与BiLSTM相结合组成AttBiLSTM预测模块进行HI的计算。最后,使用线性回归拟合来预测滚动轴承的RUL。在PHM2012数据集与XJTU-SY数据集上实验的结果表明此方法能有效预测滚动轴承的RUL。

Abstract

To accurately extract the degradation characteristics of bearings and perform effective Remaining Useful Life (RUL) prediction for rolling bearings in this paper, we propose a rolling bearing RUL prediction method based on Transient Extraction Transform and DSRNet-AttBiLSTM. Firstly, the original vibration signal is segmented and reorganized,and the time-frequency map is obtained by Transient Extracting Transform (TET). The time-frequency map is downscaled using bilinear interpolation further and channel spliced to obtain the time-frequency pictorial features of the bearing. Secondly, to accurately and efficiently extract the degradation features of rolling bearings, basic SConv and DConv modules containing depth-separable convolution and spatial-channel attention are constructed. DSRNet based on SConv and DConv is constructed to extract the bearing degradation features in both spatial and channel dimensions. Again, to make the Bidirectional Long and Short-Term Memory network pay more attention to the input features with more important information while training, an attention layer is constructed at the feature input layer. And it and BiLSTM form the AttBiLSTM prediction module to calculate the HI. Finally, linear regression fitting is used to predict the RUL of rolling bearings. The experimental results on the PHM2012 dataset and the XJTU-SY dataset show that the proposed method is effective for predicting the RUL of rolling bearings.

关键词

滚动轴承 / 剩余使用寿命 / 注意力机制 / 特征提取

Key words

rolling bearing / remaining useful life / attention mechanism / feature extraction

引用本文

导出引用
周玉国1, 张金超1, 孙伊萍1, 于春风2, 周立俭1. 基于TET与DSRNet-AttBiLSTM的滚动轴承剩余使用寿命预测[J]. 振动与冲击, 2024, 43(19): 163-173
ZHOU Yuguo1, ZHANG Jinchao1, SUN Yiping1, YU Chunfeng2, ZHOU Lijian1. Remaining useful life prediction of rolling bearings based on TET and DSRNet-AttBiLSTM[J]. Journal of Vibration and Shock, 2024, 43(19): 163-173

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