基于注意力改进卷积网络的隧道风机预埋基础损伤识别

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摘要针对隧道风机预埋基础损伤信息微弱、特征难以提取等问题，提出一种基于度量-频段注意力卷积神经网络的隧道风机预埋基础损伤识别方法。首先，通过预埋基础冲激试验的激励信号和响应信号，计算预埋基础的频率响应函数并作为损伤识别模型的输入；然后，使用多层卷积将输入的频率响应函数映射到特征空间提取损伤特征信息；再次，采用频段注意力机制对不同频段上的特征进行自适应加权融合，并采用度量损失进一步增强提取特征的可辨识性；最后，将提取的特征输入全连接层以实现预埋基础损伤识别。实测数据验证结果表明，所提方法对比现有的神经网络模型具有更高识别率，能够对隧道风机预埋基础健康状态进行有效地评估。

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	韩坤林1
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	汤宝平1
	刘大洋2
	邹小春2

关键词 ：隧道风机, 损伤识别, 深度学习, 卷积神经网络, 度量-频段注意力机制

Abstract：In order to address the issue of tedious feature extraction task caused by the weak damage information when dealing with damage identification of the embedded foundation of tunnel fan, a damage identification method based on attention convolution network is proposed. Firstly, the frequency response function is computed by applying excitation signal and response signal, and is used as the input data of damage identification model; Then, the frequency response function is mapped into the feature space by multiple convolutional layers to extract damage feature information; Thirdly, the frequency band attention mechanism is utilized to adaptively weight the features in different frequency bands, and the metric loss is employed to make the extracted features more distinguishable; Finally, the extracted features are inputted into the fully connected layer to achieve the damage recognition of the embedded foundation. The experimental results of practical measured data show that the proposed method has higher accuracy than the existing neural network models, and can effectively evaluate the health state of the embedded foundation of tunnel fan.

Key words： tunnel suspension fan damage identification deep learning convolutional neural network metric-frequency band attention mechanism

收稿日期: 2021-12-09 出版日期: 2023-05-28

引用本文:

韩坤林1,2，汤宝平1，刘大洋2，邹小春2. 基于注意力改进卷积网络的隧道风机预埋基础损伤识别[J]. 振动与冲击, 2023, 42(10): 307-313.
HAN Kunlin1,2， TANG Baoping1， LIU Dayang2， ZOU Xiaochun2. Damage identification of embedded foundation of tunnel fan based on attention improved convolution network. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(10): 307-313.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I10/307

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