基于Bi-LSTM的金属疲劳裂纹涡流脉冲热像技术检测与识别

林丽, 姜景, 朱俊臻, 冯辅周

林丽, 姜景, 朱俊臻, 冯辅周. 基于Bi-LSTM的金属疲劳裂纹涡流脉冲热像技术检测与识别[J]. 红外技术, 2023, 45(9): 982-989.
引用本文: 林丽, 姜景, 朱俊臻, 冯辅周. 基于Bi-LSTM的金属疲劳裂纹涡流脉冲热像技术检测与识别[J]. 红外技术, 2023, 45(9): 982-989.
LIN Li, JIANG Jing, ZHU Junzhen, FENG Fuzhou. Detection and Recognition of Metal Fatigue Cracks by Bi-LSTM Based on Eddy Current Pulsed Thermography[J]. Infrared Technology , 2023, 45(9): 982-989.
Citation: LIN Li, JIANG Jing, ZHU Junzhen, FENG Fuzhou. Detection and Recognition of Metal Fatigue Cracks by Bi-LSTM Based on Eddy Current Pulsed Thermography[J]. Infrared Technology , 2023, 45(9): 982-989.

基于Bi-LSTM的金属疲劳裂纹涡流脉冲热像技术检测与识别

基金项目: 

国家自然科学基金 51875576

国家自然科学基金 52005510

现代测控技术教育部重点实验开放课题 KF20211123204

详细信息
    作者简介:

    林丽(1971-),女,副教授,博士,硕士生导师,主要从事列车故障诊断方面的研究。E-mail:julandalili@126.com

    通讯作者:

    冯辅周(1971-),男,教授,博士生导师,主要从事故障诊断与无损检测技术研究。E-mail:fengfuzhou@tsinghua.org.cn

  • 中图分类号: TG115.28

Detection and Recognition of Metal Fatigue Cracks by Bi-LSTM Based on Eddy Current Pulsed Thermography

  • 摘要: 涡流脉冲热像(Eddy current pulsed thermography,ECPT)技术是一种新型的无损检测方法,广泛应用于金属材料结构的检测,但该技术常依赖人工经验提取特征进行裂纹检测与识别,自动化和智能性化程度不足。结合涡流脉冲热像技术以及循环神经网络(Recurrent Neural Network,RNN)的特性,提出一种基于双向长短期记忆网络(Bidirectional Long Short-Term Memory Network,Bi-LSTM)金属疲劳裂纹涡流脉冲热像分类识别方法。实验通过涡流加热装置对被测金属试件进行感应加热,使用红外热像采集装置对金属平板试件进行实时的数据采集,获得图像序列并制作数据集。运用设计的Bi-LSTM模型增强特征向量中的时序信息,对不同尺寸裂纹的热图像进行训练并测试。实验分析表明,Bi-LSTM网络可有效应用于金属疲劳裂纹检测与识别,针对现有裂纹检测准确率可达到100%,优于传统神经网络和其他深度学习的模型,具有更高的识别精度。
    Abstract: Eddy current pulsed thermography is a new nondestructive testing method that is widely used in metal structure testing. However, the extraction of features for crack detection and identification relies on manual experience, and the degree of automation and intelligence is insufficient. By combining the characteristics of eddy current pulsed thermography with a recurrent neural network (RNN), a bidirectional long short-term memory (Bi-LSTM)-based eddy current pulse thermography method is proposed for metal fatigue crack classification and recognition. The Bi-LSTM model was designed to enhance the transient information in the feature vectors. In the experiments, an eddy current heating device was used to heat the tested metal specimens. A real-time dataset was created using an infrared thermal camera that collected sequences of images. The Bi-LSTM model was trained on thermal images of cracks of different sizes and tested. Experimental analyses show that the Bi-LSTM network can be effectively applied for metal fatigue crack detection and recognition, with the detection accuracy reaching 100% for the cracks used in the experiments, which is superior to that of traditional neural networks and other deep learning models.
  • 图  1   LSTM神经网络结构组成

    图中:Xt表示输入状态;i是输入门;f是遗忘门;$ {\tilde c_t} $是候选记忆细胞;o是输出门;ct是记忆细胞;ht是隐藏状态。

    Figure  1.   The structure of LSTM neural network

    图  2   Bi-LSTM网络结构

    Figure  2.   Bi-LSTM network structure

    图  3   涡流脉冲热像检测实验系统

    Figure  3.   ECPT experimental system

    图  4   含疲劳裂纹的45钢平板试件

    Figure  4.   45 steel flat specimen with fatigue crack

    图  5   有裂纹数据集扩增图

    Figure  5.   Expansion of crack dataset

    图  6   无裂纹数据集扩增图

    Figure  6.   Expansion of the crack-free dataset

    图  7   文中设计模型主体结构

    Figure  7.   Main structure of the model in this paper

    图  8   训练结果曲线

    Figure  8.   Training result curves

    图  9   测试结果的准确性

    Figure  9.   Accuracy of test results

    表  1   18类金属试件裂纹长度及其编号

    Table  1   Crack length and numbering of 18 metal specimens

    Serial numbers Crack length/μm
    1 0
    2 1707.41
    3 1986.66
    4 2181.48
    5 3454.42
    6 3474.50
    7 3898.49
    8 4639.50
    9 4866.00
    10 5263.50
    11 5374.71
    12 5477.50
    13 5624.33
    14 6559.11
    15 6570.00
    16 6577.41
    17 6629.00
    18 6740.50
    下载: 导出CSV

    表  2   本文设计的模型各网络层具体参数

    Table  2   Specific parameters of each network layer in this paper

    Layer Detailed parameters
    Input 256×256,Thermal image
    Conv1 Number and size of convolution kernels,2
    5×5×1
    Conv2 Number and size of convolution kernels,4
    5×5×1
    Conv3 Number and size of convolution kernels,8
    5×5×1
    Conv4 Number and size of convolution kernels,16
    5×5×1
    Pool Number and size of convolution kernels,1
    2×2×1
    Drop Dropout (0.2)
    FC 128 fully connected layer
    Bi-LSTM1 Number of hidden layer nodes 64
    Bi-LSTM2 Number of hidden layer nodes 32
    Softmax Softmax
    下载: 导出CSV

    表  3   不同批量尺寸识别准确率

    Table  3   Different batch size identification accuracy

    Batch size Accuracy/% Time/s
    16 98.77 261
    32 99.87 220
    64 100 197
    128 94.78 162
    下载: 导出CSV

    表  4   复合检测条件下裂纹尺寸及其标签

    Table  4   Crack size and label under composite detection conditions

    Serial number Crack length/μm Serial number Crack length/μm
    a 5374.71 f 7507.79
    b 5624.33 g 7930
    c 6559.11 h 8414.54
    d 6577.41 i 9143
    e 7275 j 9453
    下载: 导出CSV

    表  5   Bi-LSTM与其他算法的实验对比

    Table  5   Experimental comparison between BI-LSTM and other algorithms

    Model Bi-LSTM (This paper) SVM KNN GooLeNet VGG ResNet
    Accuracy% 100 96.7 99.59 97.8 98.6 99.3
    Recognition time/s 197 462 293 309 345 322
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-09-21
  • 修回日期:  2022-11-22
  • 刊出日期:  2023-09-19

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