基于一维深度残差收缩网络的汇流行星排齿轮裂纹故障诊断

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摘要装甲车辆汇流行星排出现裂纹时，箱体表面振动信号干扰较多，常见的故障诊断方法存在一定的偏差，为此，提出一种利用一维深度残差收缩网络的汇流行星排齿轮裂纹故障诊断模型。其特点是将注意力机制与软阈值化结合，增强有用信息，抑制冗余信息，并将其引入到残差神经网络中，显著提高模型特征提取的能力。为了验证该模型的可行性，采集了行星轮四种不同程度裂纹的振动信号作为样本用于故障诊断。结果表明，针对汇流行星排齿轮箱振动信号该方法可以在更短的时间取得更高的准确率，相较其他方法，可以取得更好的分类结果。
关键词：深度学习;汇流行星排;故障诊断

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	田钦文1
	冯辅周2
	李鸣2
	陈晓明2
	朱俊臻2
	胡浩2
	宋超2

关键词 ：深度学习, 汇流行星排, 故障诊断

Abstract：When cracks appear in the catchment star row of armored vehicles, there is a lot of interference on the surface of the box, and there is a certain deviation in the common fault diagnosis methods. For this reason, a crack diagnosis model of confluence planetary gear based on one-dimensional depth residual shrinkage network is proposed. Its characteristic is that it combines the attention mechanism with soft threshold to enhance the useful information, restrain the redundant information, and introduce it into the residual neural network to significantly improve the ability of model feature extraction. In order to verify the feasibility of the model, the vibration signals of four kinds of planetary gear cracks are collected as samples for fault diagnosis. The results show that this method can achieve higher accuracy in a shorter time for the vibration signal of the confluence planetary gear box, and can achieve better classification results than other methods.
Key words: Deep Learning; Convergent Planetary Bar; Crack Fault Diagnosis

Key words： Deep Learning Convergent Planetary Bar Crack Fault Diagnosis

收稿日期: 2021-06-09 出版日期: 2022-10-15

引用本文:

田钦文1，冯辅周2，李鸣2，陈晓明2，朱俊臻2，胡浩2，宋超2. 基于一维深度残差收缩网络的汇流行星排齿轮裂纹故障诊断[J]. 振动与冲击, 2022, 41(19): 198-206.
TIAN Qinwen1, FENG Fuzhou2, LI Ming2, CHEN Xiaoming2, ZHU Junzhen2, HU Hao2, SONG Chao2. Gear crack fault diagnosis of convergent planetary rowbased on 1-D depth residual shrinkage network. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(19): 198-206.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I19/198

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