基于注意力机制改进残差神经网络的轴承故障诊断方法

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摘要针对滚动轴承在不同工况环境中故障诊断训练时间长、准确率低和泛化性能弱的问题，提出了基于注意力机制改进残差神经网络的轴承故障诊断方法。为了提高ResNet模型的准确率和泛化性，提出了基于注意力机制的SE-ResNet模型和CBAM-ResNet模型，并在凯斯西储大学(CWRU）数据集上进行了试验，在同工况有训练集的情况下ResNet模型测试的准确率为97.28%，在不同工况下模型直接迁移的准确率为94.14%～96.86%，CBAM-ResNet模型在不同工况下模型直接迁移的准确率为97.14%～98.86%，SE-ResNet模型在不同工况下模型直接迁移的准确率为97.86%～99.71%，两种改进模型的准确率都明显优于原ResNet模型，表明提出的优化模型提高了ResNet模型的准确率和泛化性。

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	韩争杰1
	牛荣军1
	马子魁2
	崔永存1
	邓四二1

关键词 ：深度学习, 注意力机制, 神经网络, 滚动轴承, 故障诊断

Abstract：Aiming at the problems of long training time, low accuracy and weak generalization performance of rolling bearing fault diagnosis in different working conditions, two bearing fault diagnosis methods based on attention mechanism and improved residual neural network were proposed. In order to improve the accuracy and generalization of the ResNet model, the SE-ResNet model and CBAM-ResNet model based on the attention mechanism were proposed and tested on the Case Western Reserve University (CWRU) data set. The accuracy of the ResNet model was 97.28% under the same working condition with training set. The accuracy of direct model migration under different working conditions is 94.14%-96.86%, that of CBAM-ResNet model under different working conditions is 97.14%-98.86%, that of SE-ResNet model under different working conditions is 97.86%-99.71%. The accuracy of the two improved models is significantly better than that of the original ResNet model, indicating that the proposed optimization model improves the accuracy and generalization of ResNet model.

Key words： Deep learning Attention mechanism Neural network Rolling bearing Fault diagnosis

收稿日期: 2022-08-07 出版日期: 2023-08-28

引用本文:

韩争杰1，牛荣军1，马子魁2，崔永存1，邓四二1. 基于注意力机制改进残差神经网络的轴承故障诊断方法[J]. 振动与冲击, 2023, 42(16): 82-91.
HAN Zhengjie1，NIU Rongjun1，MA Zikui2，CUI Yongcun1，DENG Sier1. Bearing fault diagnosis methods based on an attentional-mechanism-improved residual neural network. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(16): 82-91.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I16/82

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