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.
-
-
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 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
5×5×1Conv2 Number and size of convolution kernels,4
5×5×1Conv3 Number and size of convolution kernels,8
5×5×1Conv4 Number and size of convolution kernels,16
5×5×1Pool Number and size of convolution kernels,1
2×2×1Drop 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 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 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 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 -
[1] 韩丽民. 采煤机关键部件故障分析与诊断[J]. 能源与节能, 2021(11): 156-157, 159. DOI: 10.16643/j.cnki.14-1360/td.2021.11.061. HAN limin. Fault analysis and diagnosis of key parts of shearer[J]. Energy and Conservation, 2021(11): 156-157, 159. DOI: 10.16643/j.cnki.14-1360/td.2021.11.061
[2] 窦建. 机电设备维修管理的现状和对策认识实践[J]. 中国设备工程, 2022(13): 79-81. https://www.cnki.com.cn/Article/CJFDTOTAL-SBGL202213036.htm DOU Jian. Current situation and countermeasures of maintenance management of mechanical and electrical equipment[J]. China Equipment Engineering, 2022(13): 79-81. https://www.cnki.com.cn/Article/CJFDTOTAL-SBGL202213036.htm
[3] 冯辅周, 朱俊臻, 闵庆旭, 等. 涡流热像无损检测技术综述[J]. 装甲兵工程学院学报, 2016, 30(6): 60-67. https://www.cnki.com.cn/Article/CJFDTOTAL-ZJBX201606012.htm FENG Fuzhou, ZHU Junzhen, MIN Qingxu, et al. Review of eddy current thermography nondestructive testing[J]. Journal of Academy of Armored Force Engineering, 2016, 30(6): 60-67. https://www.cnki.com.cn/Article/CJFDTOTAL-ZJBX201606012.htm
[4] 苗玲, 高斌, 石永生, 等. 基于电涡流热成像的钢轨滚动接触疲劳裂纹动态检测研究[J]. 机械工程学报, 2021, 57(18): 86-97. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202118011.htm MIAO Ling, GAO Bin, SHI Yongsheng, et al. Dynamic detection of rolling contact fatigue cracks in rail based on eddy current thermal imaging[J]. Journal of Mechanical Engineering, 2021, 57(18): 86-97. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202118011.htm
[5] 毕野, 熊新, 叶波, 等. 基于深度学习的涡流热成像技术在无损检测中的应用[J]. 化工自动化及仪表, 2019, 46(9): 690-696. https://www.cnki.com.cn/Article/CJFDTOTAL-HGZD201909002.htm BI Ye, XIONG Xin, YE Bo, et al. Application of eddy current thermal imaging technology based on deep learning in nondestructive testing[J]. Chemical Automation and Instrumentation, 2019, 46(9): 690-696. https://www.cnki.com.cn/Article/CJFDTOTAL-HGZD201909002.htm
[6] GAO B, LI X, WOO W L, et al. Quantitative validation of eddy current stimulated thermal features on surface crack[J]. Ndt & E International, 2017, 85: 1-12.
[7] 孙吉伟, 孙浩, 谢敏, 等. 涡流脉冲热像技术中基于神经网络的检出/漏检预测研究[J]. 红外技术, 2020, 42(8): 795-800. http://hwjs.nvir.cn/article/id/hwjs202008015 SUN Jiwei, SUN Hao, XIE Min, et al. Prediction of detection detection based on neural network in eddy current pulse thermography[J]. Infrared Technology, 2020, 42(8): 795-800. http://hwjs.nvir.cn/article/id/hwjs202008015
[8] 刘建伟, 宋志妍. 循环神经网络研究综述[J]. 控制与决策, 2022, 37(11): 16. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC202211001.htm LIU Wei, SONG Zhiyan. Review of recurrent Neural networks[J]. Control and Decision Making, 2022, 37(11): 16. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC202211001.htm
[9] YU Y, SI X, HU C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural Computation, 2019, 31(7): 1235-1270.
[10] Crisóstomo de Castro Filho H, Abílio de Carvalho Júnior O, Ferreira de Carvalho O L, et al. Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series[J]. Remote Sensing, 2020, 12(16): 2655.
[11] TIAN G, WANG Q, ZHAO Y, et al. Smart contract classification with a bi-LSTM based approach[J]. IEEE Access, 2020, 8: 43806-43816.
[12] 高玉才, 付忠广, 王诗云, 等. 基于Bi-LSTM和自注意力机制的旋转机械故障诊断方法研究[J]. 中国工程机械学报, 2022, 20(3): 273-278. https://www.cnki.com.cn/Article/CJFDTOTAL-GCHE202203017.htm GAO Yucai, FU Zhongguang, WANG Shiyun. Research on fault diagnosis method of rotating machinery based on BI-LSTM and self-attention mechanism[J]. Chinese Journal of Construction Machinery, 2022, 20(3): 273-278. https://www.cnki.com.cn/Article/CJFDTOTAL-GCHE202203017.htm
[13] 温惠英, 张东冉, 陆思园. GA-LSTM模型在高速公路交通流预测中的应用[J]. 哈尔滨工业大学学报, 2019, 51(9): 81-87, 95. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201909012.htm WEN Huiying, ZHANG Dongran, LU Siyuan. Application of GA-LSTM model in expressway traffic flow prediction[J]. Journal of Harbin Institute of Technology, 2019, 51(9): 81-87, 95. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201909012.htm
[14] MIN Q, ZHU J, SUN J, et al. Investigation of heat source reconstruction of thickness-through fatigue crack using lock-in vibrothermography[J]. Infrared Physics & Technology, 2018, 94: 291-298.
[15] 林丽, 刘新, 朱俊臻, 等. 基于CNN的金属疲劳裂纹超声红外热像检测与识别方法研究[J]. 红外与激光工程, 2022, 51(3): 475-483. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202203042.htm LIN Li, LIU Xin, ZU Junzhen, et al. Research on detection and identification of metal fatigue crack by ultrasonic infrared thermography based on CNN[J]. Infrared and Laser Engineering, 2022, 51(3): 475-483. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202203042.htm
[16] 纪盟盟, 肖金壮, 李瑞鹏. CNN联合BI-LSTM混合模型的手势识别算法[J]. 激光杂志, 2021, 42(6): 88-91. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202106019.htm JI Mengmeng, XIAO Jinzhuang, LI Ruipeng. Gesture recognition algorithm based on CNN combined with BI-LSTM hybrid model[J]. Laser Magazine, 2021, 42(6): 88-91. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202106019.htm
[17] 于洋, 马军, 王晓东, 等. 融合深度可分离小卷积核和CBAM的改进CNN故障诊断模型[J]. 电子测量技术, 2022, 45(6): 171-178. https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL202206027.htm YU Yang, MA Jun, WANG Xiaodong, et al. Improved CNN fault diagnosis model by fusing deep separable small convolution kernel and CBAM[J]. Electronic Measurement Technique, 2022, 45(6): 171-178. https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL202206027.htm
[18] GAO Hongbin, ZHANG Ya, LV Wenkai, et al. A deep convolutional generative adversarial networks-based method for defect detection in small sample industrial parts images[J]. Applied Sciences, 2022, 12(13): 6569.
[19] JIAO J, ZHAO M, LIN J, et al. A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes[J]. Knowledge-Based Systems, 2018, 160: 237-250.
[20] 刘杰, 丁武学, 孙宇, 等. 基于SVM的搅拌摩擦焊表面缺陷分类[J]. 组合机床与自动化加工技术, 2022(3): 130-133, 137. https://www.cnki.com.cn/Article/CJFDTOTAL-ZHJC202203031.htm LIU Jie, DING Xuewu, SUN Yu, et al. Surface defect classification of friction stir welding based on SVM[J]. Modular Machine Tools and Automatic Processing Technology. 2022(3): 130-133, 137. https://www.cnki.com.cn/Article/CJFDTOTAL-ZHJC202203031.htm
[21] GUO J, WANG X. Image classification based on SURF and KNN[C]//IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS), 2019: 356-359.
[22] MA J, RAO J, QIAO Y, et al. Sprouting potato recognition based on deep neural network GoogLeNet[C]//IEEE 3rd International Conference on Cloud Computing and Internet of Things (CCIOT), 2018: 502-505.
[23] DUAN C, YIN P, ZHI Y, et al. Image classification of fashion-MNIST data set based on VGG network[C]//Proceedings of 2019 2nd International Conference on Information Science and Electronic Technology (ISET). International Informatization and Engineering Associations: Computer Science and Electronic Technology International Society, 2019: 19.
[24] Mahajan A, Chaudhary S. Categorical image classification based on representational deep network (RESNET)[C]//2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), 2019: 327-330.
-
期刊类型引用(3)
1. 韩文斌. 大型水利工程施工中混凝土大坝活动裂纹检测方法. 水上安全. 2025(01): 103-105 . 百度学术
2. 蔡云程. 船用涡流检测技术在船舶结构无损检验中的应用研究. 仪器仪表用户. 2024(06): 101-103+106 . 百度学术
3. 曾俊恺. 基于声发射技术的压力容器管道裂纹扩展无损检测方法分析. 中国机械. 2024(31): 126-130 . 百度学术
其他类型引用(0)