Damage identification of embedded foundation of tunnel fan based on attention improved convolution network

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (10) : 307-313.

HAN Kunlin1,2， TANG Baoping1， LIU Dayang2， ZOU Xiaochun2

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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

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HAN Kunlin1,2， TANG Baoping1， LIU Dayang2， ZOU Xiaochun2. Damage identification of embedded foundation of tunnel fan based on attention improved convolution network[J]. Journal of Vibration and Shock, 2023, 42(10): 307-313

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