卓德兵, 曹晖. 基于小波时频图与轻量级卷积神经网络的螺栓连接损伤识别[J]. 工程力学, 2021, 38(9): 228-238. DOI: 10.6052/j.issn.1000-4750.2021.02.0116
引用本文: 卓德兵, 曹晖. 基于小波时频图与轻量级卷积神经网络的螺栓连接损伤识别[J]. 工程力学, 2021, 38(9): 228-238. DOI: 10.6052/j.issn.1000-4750.2021.02.0116
ZHUO De-bing, CAO Hui. DAMAGE IDENTIFICATION OF BOLT CONNECTIONS BASED ON WAVELET TIME-FREQUENCY DIAGRAMS AND LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORKS[J]. Engineering Mechanics, 2021, 38(9): 228-238. DOI: 10.6052/j.issn.1000-4750.2021.02.0116
Citation: ZHUO De-bing, CAO Hui. DAMAGE IDENTIFICATION OF BOLT CONNECTIONS BASED ON WAVELET TIME-FREQUENCY DIAGRAMS AND LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORKS[J]. Engineering Mechanics, 2021, 38(9): 228-238. DOI: 10.6052/j.issn.1000-4750.2021.02.0116

基于小波时频图与轻量级卷积神经网络的螺栓连接损伤识别

DAMAGE IDENTIFICATION OF BOLT CONNECTIONS BASED ON WAVELET TIME-FREQUENCY DIAGRAMS AND LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORKS

  • 摘要: 针对目前大型结构螺栓连接状态监测的困难,该文采用声音信号,提出了结合小波时频图与轻量级卷积神经网络MobileNetv2优势的螺栓松动识别方法。该方法通过对采集到的声音信号进行预处理和连续小波变换得到小波时频图,以小波时频图作为样本对轻量级卷积神经网络MobileNetv2进行训练,从而实现螺栓松动声音信号的识别。对一钢桁架模型的室外试验研究表明:该方法能实现对各种环境噪声信号,不同位置、数目和松动程度的螺栓松动声音信号的精准识别;该方法不仅识别准确率高、稳定性好,而且对计算和存储的要求低,便于应用于移动设备和嵌入式设备,为环境激励下大型复杂结构的损伤在线识别提供了新的思路。

     

    Abstract: In view of the difficulty of monitoring the states of bolt connections of large-scale structures, proposed a method for bolt looseness recognition by sound signals, which takes the advantages of the wavelet time-frequency analysis and the powerful image classification ability of the lightweight convolution neural network MobileNetv2. Continuous wavelet transform was carried out for the preprocessed sound signals to obtain the wavelet time-frequency diagrams. The lightweight convolutional neural network MobileNetv2 was trained using the wavelet time-frequency diagrams as samples. The trained model was used to identify the sound signals generated by loosen bolts. An outdoor test of a steel truss model showed that the proposed method could accurately recognize the sound signals of loosen bolts at different positions, with different numbers and different degrees of looseness, and with various environmental noise signals. This novel method has high identification accuracy and good stability, and requires low calculation cost and storage space. It can be carried out easily by mobile devices and embedded devices, providing a new idea for online damage recognition of large and complex structures under environmental excitation.

     

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