基于注意力模块及1D-CNN的滚动轴承故障诊断

刘洋; 程强; 史曜炜; 王煜伟; 王姗; 邓艾东

doi:10.19912/j.0254-0096.tynxb.2020-0495

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (3) : 462-468. DOI: 10.19912/j.0254-0096.tynxb.2020-0495

基于注意力模块及1D-CNN的滚动轴承故障诊断

刘洋¹, 程强¹, 史曜炜¹, 王煜伟², 王姗³, 邓艾东¹

作者信息 +

FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON ATTENTION MODULE AND 1D-CNN

Liu Yang¹, Cheng Qiang¹, Shi Yaowei¹, Wang Yuwei², Wang Shan³, Deng Aidong¹

Author information +

文章历史 +

摘要

针对传统的卷积神经网络对特征的辨识性差的问题,提出一种将注意力模块与一维卷积神经网络相结合的滚动轴承故障诊断模型。首先以加入噪声的振动信号作为输入,利用“卷积+池化”单元提取信号的多维特征,然后通过注意力模块对特征赋予不同的权重,利用双池化层取代传统卷积神经网络中的全连接层进行特征的再次提取及特征信息整合,最后通过Softmax层完成轴承状态分类。实验表明,该模型诊断准确率可达99%,与传统模型相比,其准确率更高、收敛速度更快、训练过程更稳定、变负载情况下泛化性能更好。

Abstract

Aiming at the problem of poor feature recognition ability of traditional convolutional neural network（CNN）, this paper proposes a rolling bearings fault diagnosis model combining the attention module and the one-dimensional convolutional neural network （1D-CNN）. Firstly, the noise-added vibration signals are used as the input of the proposed model, and their multi-dimensional features are extracted by using the “convolution + pooling” unit. Then, the attention module assigns different weights to the extracted features. The double pooling layer is adopted to replace the fully connected layer in the traditional CNN for feature extraction and information integration. Finally, a Softmax layer is used to achieve bearing status classification. Experimental results show that the diagnostic accuracy of the model proposed in this paper reaches 99%. Compared with the traditional models, the proposed model has higher diagnostic accuracy, faster convergence speed, a more stable training process, and better generalization performance under variable load.

导出引用

刘洋, 程强, 史曜炜, 王煜伟, 王姗, 邓艾东. 基于注意力模块及1D-CNN的滚动轴承故障诊断[J]. 太阳能学报. 2022, 43(3): 462-468 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0495

Liu Yang, Cheng Qiang, Shi Yaowei, Wang Yuwei, Wang Shan, Deng Aidong. FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON ATTENTION MODULE AND 1D-CNN[J]. Acta Energiae Solaris Sinica. 2022, 43(3): 462-468 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0495

中图分类号： TH133.33

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