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

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

卓德兵^{1, 2, 3,},
曹晖^{1, 2, ,}

1.
重庆大学土木工程学院，重庆 400045
2.
重庆大学山地城镇建设与新技术教育部重点实验室，重庆 400045
3.
吉首大学土木工程与建筑学院，张家界 427000

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
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- 文章访问数: 80
- HTML全文浏览量: 28
- PDF下载量: 27
- 被引次数: 0
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

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

ZHUO De-bing^{1, 2, 3
,},
CAO Hui^{1, 2
, ,}

1.
College of Civil Engineering, Chongqing University, Chongqing 400045, China
2.
MOE Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing 400045, China
3.
School of civil engineering and architecture, Jishou University, Zhangjiajie 427000, China

摘要

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。
- 螺栓连接 /
- 损伤识别 /
- 声音信号 /
- 小波时频图 /
- MobileNetv2
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.
- bolt connection /
- damage identification /
- sound signal /
- wavelet time-frequency diagram /
- MobileNetv2

HTML全文

图 1 MobileNetv2卷积过程

Figure 1. Convolution process of MobileNetv2

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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

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图 3 试验模型及测试系统

Figure 3. Test model and test system

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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

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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

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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

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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

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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

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图 9 混淆矩阵

Figure 9. Confusion matrix

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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
224²×3	Conv2d	−	32	1	2
112²×32	bottleneck	1	16	1	1
112²×16	bottleneck	6	24	2	2
56²×24	bottleneck	6	32	3	2
28²×32	bottleneck	6	64	4	2
14²×64	bottleneck	6	96	3	1
14²×96	bottleneck	6	160	3	2
7²×160	bottleneck	6	320	1	1
7²×320	conv2d 1×1	−	1280	1	1
7²×1280	avgpool 7×7	−	−	1	−
1×1×1280	conv2d 1×1	−	k	−	−

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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1	J₁₅	1	0.5
工况2	J₁₅	1	1.0
工况3	J₁₅	1	1.5
工况4	J₁₅	2	0.5
工况5	J₁₅	3	0.5
工况6	J₂₄	1	0.5
工况7	J₂₄	2	0.5
工况8	J₃₆	1	0.5
工况9	J₃₆	2	0.5
工况10	J₄₂	1	0.5

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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
CPU	Intel Xeon E5-2650 V4
GPU	NVIDA 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

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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
项目	训练集/(%)	验证集/(%)	训练集	验证集
AlexNet	99.34	98.63	0.04	0.07
GoogleNet	99.70	99.38	0.02	0.03
VGG16	100.00	99.60	0.00	0.01
ResNet50	100.00	99.90	0.00	0.00
MobileNetv2	100.00	100.00	0.00	0.00

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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
AlexNet	1.5×10⁷	56 M
GoogleNet	7.0×10⁷	268 M
VGG16	1.0×10⁷	39 M
ResNet50	2.1×10⁷	90 M
MobileNetv2	0.2×10⁷	9 M

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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
A	1.000	0.98	1.000
B	1.000	1.00	1.000
C	0.873	0.96	0.991
D	1.000	1.00	1.000
E	1.000	0.94	1.000
F	1.000	1.00	1.000
G	1.000	1.00	1.000
H	1.000	1.00	1.000
I	0.907	0.98	0.993
J	1.000	0.84	1.000
K	1.000	1.00	1.000
L	0.943	1.00	0.996
M	1.000	1.00	1.000
N	0.980	0.98	0.999
O	1.000	1.00	1.000
P	1.000	1.00	1.000

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