一种机械设备故障诊断的FD-Transformer方法

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摘要随着机械设备故障诊断技术的发展，利用深度学习技术判断设备故障类型越来越引起人们的重视。目前，基于注意力机制的Transformer模型有着优于卷积神经网络(convolutional neural network,CNN)的特征提取能力且在自然语言处理及计算机视觉领域都得到了成功的应用。提出一种用于机械设备故障诊断的Transformer方法（FaultDiagnosis-Transformer，简称FD-Transformer），首先对原始振动信号利用Dropout技术进行数据增强，提高模型的泛化能力；然后利用多通道一维卷积进行数据处理并得到矩阵形式；接着利用Dense连接的Encoder结构来进行机械设备的故障特征提取，最后利用分类模块得到故障诊断结果。分别采用变转速轴承数据和轮对轴承数据对模型进行实验验证，实验结果表明，该模型在两种数据集上均达到了99%以上的故障识别率，与CNN相比可以更好地提取机械设备故障特征，有一定的工程应用价值。

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	赵志宏1
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	李春秀2
	李乐豪2
	杨绍普1

关键词 ： Transformer, 注意力机制, 故障诊断, 深度学习

Abstract：With the development of mechanical equipment fault diagnosis technology, people pay more and more attention to using deep learning technology to judge the type of equipment fault. At present, Transformer model based on attention mechanism has better feature extraction ability than Convolutional Neural Network, and has been successfully applied in the fields of natural language processing and computer vision. A Transformer method for mechanical equipment fault diagnosis (FD-Transformer) is proposed. Firstly, the original vibration signal is enhanced by dropout technology to improve the generalization ability of the model; Then the matrix form is obtained by multi-channel one-dimensional convolution; Then, the Encoder structure connected by Dense is used to extract the fault features of mechanical equipment. Finally, the fault diagnosis results are obtained by using the classification module. The experimental results show that the model achieves a fault recognition rate of more than 99% on both data sets. Compared with CNN, it can better extract the fault characteristics of mechanical equipment, and has certain engineering application value.

Key words： Transformer Attention mechanism fault diagnosis deep learning

收稿日期: 2022-02-24 出版日期: 2023-04-28

引用本文:

赵志宏1，2，李春秀2，李乐豪2，杨绍普1. 一种机械设备故障诊断的FD-Transformer方法[J]. 振动与冲击, 2023, 42(8): 326-333.
ZHAO Zhihong1,2, LI Chunxiu2, LI Lehao2, YANG Shaopu1. A FD-Transformer method for fault diagnosis of mechanical equipment. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(8): 326-333.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I8/326

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