基于深度残差收缩迁移网络的复杂工况下滚动轴承故障诊断

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (3) : 194-200.

论文

陈仁祥1，张晓1，朱玉清1，徐向阳1，杨宝军2

作者信息 +

Fault diagnosis of rolling bearing under complex operating conditions based on deep residual shrinkage transfer network

CHEN Renxiang1, ZHANG Xiao1, ZHU Yuqing1, XU Xiangyang1, YANG Baojun2

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文章历史 +

摘要

针对噪声和不同转速的复杂工况下滚动轴承故障诊断问题，提出一种基于深度残差收缩迁移网络的复杂工况下滚动轴承故障诊断方法。在深度残差收缩网络中加入领域适配层构建出具备降噪与适配能力的深度残差收缩迁移网络，从而减小噪声带来的干扰及转速变化导致的分布差异。首先，利用注意力机制学习经卷积层后各特征通道的重要性自动设定一组阈值，借助软阈值化将阈值范围内的特征置为零，减少噪声带来的干扰；然后，通过边缘分布适配对齐两域特征分布，减小转速变化带来的分布差异；最后，在Softmax分类层下实现端到端的复杂工况下的滚动轴承故障诊断。复杂工况下滚动轴承故障诊断实验验证了所提方法的可行性和有效性。

Abstract

Aiming the diagnosis of rolling bearing faults in complex operating conditions with strong noise and different speeds. Rolling bearing fault diagnosis method under complex working conditions based on deep residual shrinkage transfer network (DRSTN) was proposed. The domain adaptation layer is added to the deep residual shrinkage network to construct the deep residual shrinkage migration network with noise reduction and adaptation ability, so as to reduce the interference caused by noise and the distribution difference caused by speed change. Firstly, a set of thresholds are automatically set by using the attention mechanism to learn the importance of each feature channel after convolution layer, and the features in the threshold range are set to zero by soft threshold to reduce the interference caused by noise. Then, the feature distribution of the two domains is aligned by edge distribution to reduce the distribution difference caused by speed change. Finally, the fault diagnosis of rolling bearing under end-to-end complex conditions is realized under Softmax classification layer. The experimental results of rolling bearing fault diagnosis under complex conditions verify the feasibility and effectiveness of the proposed method.

导出引用

陈仁祥1，张晓1，朱玉清1，徐向阳1，杨宝军2. 基于深度残差收缩迁移网络的复杂工况下滚动轴承故障诊断[J]. 振动与冲击, 2024, 43(3): 194-200

CHEN Renxiang1, ZHANG Xiao1, ZHU Yuqing1, XU Xiangyang1, YANG Baojun2. Fault diagnosis of rolling bearing under complex operating conditions based on deep residual shrinkage transfer network[J]. Journal of Vibration and Shock, 2024, 43(3): 194-200

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