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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

卓德兵 曹晖

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

doi: 10.6052/j.issn.1000-4750.2021.02.0116
基金项目: 湖南省教育厅科学研究一般项目(20C1512)
详细信息
    作者简介:

    卓德兵(1985−),男,湖南人,讲师,博士,主要从事结构健康监测研究(E-mail: zhuodebing2004@163.com)

    通讯作者:

    曹 晖(1969−),男,四川人,教授,博士,博导,主要从事结构健康监测研究(E-mail: caohui@cqu.edu.cn)

  • 中图分类号: TU317+.1

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。
  • 图  1  MobileNetv2卷积过程

    Figure  1.  Convolution process of MobileNetv2

    图  2  小波时频图- MobileNetv2螺栓松动损伤识别方法结构图

    Figure  2.  Structural diagram of bolt loosening damage identification method based on wavelet time frequency diagrams and MobileNetv2

    图  3  试验模型及测试系统

    Figure  3.  Test model and test system

    图  4  蝉鸣声信号图及短时能量最大帧的分析结果

    Figure  4.  Cicada sound signal diagram and analysis results of frame with the largest short-time energy

    图  5  下雨声信号图及短时能量最大帧的分析结果

    Figure  5.  Rain sound signal diagram and analysis results of frame with the largest short-time energy

    图  6  工况6信号图及短时能量最大帧的分析结果

    Figure  6.  Signal diagram of Case 6 and analysis results of frame with the largest short-time energy

    图  7  样本帧长对MobileNetv2模型识别结果的影响

    Figure  7.  Influence of sample frame length on recognition results of MobileNetv2 model

    图  8  MobileNetv2模型与不同CNN模型的训练结果对比

    Figure  8.  Comparison of training results between MobileNetv2 and other CNN models

    图  9  混淆矩阵

    Figure  9.  Confusion matrix

    表  1  网络结构模型

    Table  1.   Network structure model

    输入操作扩展
    因子
    输出特征
    矩阵深度
    重复
    操作次数
    第一层
    步长
    2242×3Conv2d3212
    1122×32bottleneck11611
    1122×16bottleneck62422
    562×24bottleneck63232
    282×32bottleneck66442
    142×64bottleneck69631
    142×96bottleneck616032
    72×160bottleneck632011
    72×320conv2d 1×1128011
    72×1280avgpool 7×71
    1×1×1280conv2d 1×1k
    下载: 导出CSV

    表  2  损伤工况

    Table  2.   Damage cases

    工况松动位置松动个数松动程度/圈
    工况1J1510.5
    工况2J1511.0
    工况3J1511.5
    工况4J1520.5
    工况5J1530.5
    工况6J2410.5
    工况7J2420.5
    工况8J3610.5
    工况9J3620.5
    工况10J4210.5
    下载: 导出CSV

    表  3  计算机平台及环境配置

    Table  3.   Computer platform and environment configuration

    软硬件平台型号参数
    操作系统Windows 10 64位系统
    CPUIntel Xeon E5-2650 V4
    GPUNVIDA TESLA P100 16G
    内存16 G
    编程环境Anaconda 3
    CUDA 10.0
    Cudnn 7.4.1.5
    Python 3.7 64位
    Tensorflow-gpu 1.13.2
    Numpy 1.17.4
    Keras 2.1.5
    下载: 导出CSV

    表  4  各模型迭代100次的分类准确率和损失值

    Table  4.   Classification accuracy and loss of each model after 100 iterations

    项目准确率损失值
    训练集/(%)验证集/(%)训练集验证集
    AlexNet99.3498.630.040.07
    GoogleNet99.7099.380.020.03
    VGG16100.0099.600.000.01
    ResNet50100.0099.900.000.00
    MobileNetv2100.00100.000.000.00
    下载: 导出CSV

    表  5  各模型100次迭代后的总参数量和权重文件大小

    Table  5.   Total parameters and weight file size of each model after 100 iterations

    项目总参数量权重文件大小
    AlexNet1.5×10756 M
    GoogleNet7.0×107268 M
    VGG161.0×10739 M
    ResNet502.1×10790 M
    MobileNetv20.2×1079 M
    下载: 导出CSV

    表  6  分类精确率、召回率和特异度

    Table  6.   Precision, recall and specificity

    标签精确率召回率特异度
    A1.0000.981.000
    B1.0001.001.000
    C0.8730.960.991
    D1.0001.001.000
    E1.0000.941.000
    F1.0001.001.000
    G1.0001.001.000
    H1.0001.001.000
    I0.9070.980.993
    J1.0000.841.000
    K1.0001.001.000
    L0.9431.000.996
    M1.0001.001.000
    N0.9800.980.999
    O1.0001.001.000
    P1.0001.001.000
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-02
  • 修回日期:  2021-07-11
  • 网络出版日期:  2021-07-22
  • 刊出日期:  2021-09-13

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