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基于内积矩阵及卷积自编码器的螺栓松动状态监测

张敏照 王乐 田鑫海

张敏照, 王乐, 田鑫海. 基于内积矩阵及卷积自编码器的螺栓松动状态监测[J]. 工程力学, 2022, 39(12): 222-231. doi: 10.6052/j.issn.1000-4750.2021.07.0583
引用本文: 张敏照, 王乐, 田鑫海. 基于内积矩阵及卷积自编码器的螺栓松动状态监测[J]. 工程力学, 2022, 39(12): 222-231. doi: 10.6052/j.issn.1000-4750.2021.07.0583
ZHANG Min-zhao, WANG Le, TIAN Xin-hai. BOLT LOOSENING STATE MONITORING BASED ON INNER PRODUCT MATRIX AND CONVOLUTIONAL AUTOENCODER[J]. Engineering Mechanics, 2022, 39(12): 222-231. doi: 10.6052/j.issn.1000-4750.2021.07.0583
Citation: ZHANG Min-zhao, WANG Le, TIAN Xin-hai. BOLT LOOSENING STATE MONITORING BASED ON INNER PRODUCT MATRIX AND CONVOLUTIONAL AUTOENCODER[J]. Engineering Mechanics, 2022, 39(12): 222-231. doi: 10.6052/j.issn.1000-4750.2021.07.0583

基于内积矩阵及卷积自编码器的螺栓松动状态监测

doi: 10.6052/j.issn.1000-4750.2021.07.0583
详细信息
    作者简介:

    张敏照(1995−),女,甘肃张掖人,博士生,主要从事结构健康监测研究(E-mail: minzhaozhang@mail.nwpu.edu.cn)

    田鑫海(1998−),男,贵州铜仁人,硕士生,主要从事结构健康监测研究(E-mail: txh@mail.nwpu.edu.cn)

    通讯作者:

    王 乐(1984−),男,陕西西安人,副教授,博士,主要从事结构健康监测与模型修正研究(E-mail: le.wang@nwpu.edu.cn)

  • 中图分类号: TP181

BOLT LOOSENING STATE MONITORING BASED ON INNER PRODUCT MATRIX AND CONVOLUTIONAL AUTOENCODER

  • 摘要: 螺栓连接结构中的螺栓松动容易导致结构失效,如何对结构中的螺栓松动状态进行监测是当前研究的一个热点。该文利用环境激励下结构振动响应的相关性分析,结合深度学习技术,研究了一种联合使用内积矩阵(inner product matrix, IPM)和卷积自编码器(convolutional autoencoder, CAE)的神经网络模型,即基于内积矩阵及卷积自编码器(inner product matrix and convolutional autoencoder, IPM-CAE)的深度学习模型。通过对螺栓连接搭接板的螺栓松动状态监测的试验研究,验证了该方法的可行性及有效性,并与使用 IPM的卷积神经网络(convolutional neural network, CNN)、堆栈自动编码器(stack autoencoder, SAE)及胶囊网络(capsule network, CapsNet)相比,IPM-CAE方法具有较快的网络训练收敛速度和较高的识别精度。
  • 图  1  CAE网络结构

    Figure  1.  Network structure of CAE

    图  2  螺栓松动状态监测过程图

    Figure  2.  Process diagram of bolt loosening state monitoring

    图  3  实验示意图

    Figure  3.  Experimental schematic

    图  4  实验现场

    Figure  4.  Experimental setup

    图  5  1号螺栓健康状态和部分损伤状态响应图

    Figure  5.  Responses of No. 1 bolt health status and partial damage status

    图  6  6个螺栓的松动状态监测结果

    Figure  6.  Monitoring results of the loose states of 6 bolts

    图  7  6个螺栓的预紧扭矩混淆矩阵

    Figure  7.  Confusion matrix of 6 bolts pre-tightening torques

    图  8  不同网络的训练曲线

    Figure  8.  Training curves of different networks

    表  1  实验编号设置

    Table  1.   Experiment number setting

    螺栓编号 状态编号 预紧扭矩/(N·m) 健康状态
    1 1-5 5.0 健康
    1-4.5 4.5 损伤
    1-4 4.0 损伤
    1-3.5 3.5 损伤
    1-3 3.0 损伤
    1-2.5 2.5 损伤
    1-2 2.0 损伤
    1-1.5 1.5 损伤
    1-1 1.0 损伤
    1-0.5 0.5 损伤
    1-0 0.0 损伤
    下载: 导出CSV

    表  2  CAE模型结构对监测结果的影响

    Table  2.   Effect of the structure of different CAE models on the detection results

    名称 卷积层1 卷积层2 卷积层3 卷积核大小 n m 准确率
    CAE_1 28 (3,3) 8192 1024 0.964
    CAE_2 56 (3,3) 8192 1024 0.972
    CAE_3 128 (3,3) 8192 1024 0.984
    CAE_4 28 14 (3,3) 8192 1024 0.974
    CAE_5 56 28 (3,3) 8192 1024 0.981
    CAE_6 128 56 (3,3) 8192 1024 0.990
    CAE_7 28 14 8 (3,3) 8192 1024 0.981
    CAE_8 56 28 8 (3,3) 8192 1024 0.988
    CAE_9 128 56 8 (3,3) 8192 1024 0.987
    下载: 导出CSV

    表  3  最佳的CAE网络结构

    Table  3.   The best structure of CAE network

    类型 输入 输出 深度 滤波器大小 步幅 激活函数
    InputLayer (None, 8, 8, 1) (None, 8, 8, 1) None None None None
    Convolution (None, 8, 8, 1) (None, 8, 8, 128) 128 (3,3) 1 relu
    Convolution (None, 8, 8, 128) (None, 8, 8, 56) 56 (3,3) 1 relu
    MaxPooling2D (None, 8, 8, 56) (None, 4, 4, 56) None (2,2) 2 None
    Flatten (None, 4, 4, 56) (None, 896) None None None None
    Fully Connected (None, 896) (None, 32) None None None relu
    softmax (None, 32) (None, 11) None None None softmax
    下载: 导出CSV

    表  4  6个螺栓测试集准确率

    Table  4.   Accuracy of 6 bolts test sets

    编号 测试集准确率
    1 0.99
    2 0.98
    3 0.98
    4 0.98
    5 0.97
    6 0.98
    下载: 导出CSV

    表  5  不同网络的对比结果

    Table  5.   Comparison results of different networks

    网络名称 准确率 损失
    SAE 0.97 0.07
    Capsnet 0.90 0.02
    CNN 0.96 0.09
    CAE 0.99 0.05
    下载: 导出CSV

    表  6  不同采样点数和测点数量下的准确率对比

    Table  6.   Comparison of accuracy rates under different sample points and measurement points

    采样点数 6测点 4测点 2测点
    8192 0.955 0.910 0.531
    9216 0.989 0.928 0.535
    10240 0.993 0.936 0.542
    11264 0.995 0.959 0.549
    下载: 导出CSV
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  • 收稿日期:  2021-07-29
  • 修回日期:  2021-12-04
  • 网络出版日期:  2022-02-17
  • 刊出日期:  2022-12-01

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