Infrared and Laser Engineering, Volume. 51, Issue 3, 20210227(2022)
Research on vibrothermography detection and recognition method of metal fatigue cracks based on CNN
[1] [1] Fan Xinguang. Study of fatigue crack initiation propagation of railroad wheel under rolling contact[D]. Beijing: Beijing Jiaotong University, 2019. (in Chinese)
[2] Gong Ke, Wu Ming, Xie Fei, et al. Effect of dry/wet ratio and pH on the stress corrosion cracking behavior of rusted X100 steel in an alternating dry/wet environment[J]. Construction and Building Materials, 270, 124826(2020).
[3] Zhou Zhixin. Overview of NDT methods for mechanical cracks[J]. Mechanical and Electrical Engineering, 34, 1138-1143(2017).
[4] Tang Changming, Zhong Jianfeng, Zhong Shuncong, et al. Ultrasound infrared thermography defect recognition based on improved adaptive genetic algorithm with two-dimensional maximum entropy[J]. Infrared Technoloy, 42, 801-808(2020).
[5] Ji Longxin, Feng Fuzhou, Min Qingxu. Ultrasonic infrared thermal image processing based on wavelet transform[J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 43, 112-116, 128(2020).
[6] He Yunze, Deng Baoyuan, Wang Hongjin, et al. Infrared machine vision and infrared thermography with deep learning: A review[J]. Infrared Physics & Technology, 116, 103754(2021).
[7] Chang Ying, Chang Dajun. Research on solder joint defect recognition algorithm based on improved convolutional neural network[J]. Laser Technology, 44, 779-783(2020).
[8] Liu Bingji, Xiong Bangshu, Ou Qiaofeng, et al. Fault diagnosis of rolling bearing based on time-frequency representations and CNN[J]. Journal of Nanchang Hangkong University (Natural Science Edition), 32, 86-91(2018).
[9] Renshaw J, Chen J C, Holland S D, et al. The sources of heat generation in vibrothermography[J]. NDT and E International, 44, 736-739(2011).
[10] Min Qingxu, Zhu Junzhen, Feng Fuzhou, et al. Study on optimization method of test conditions for fatigue crack detection using lock-in vibrothermography[J]. Infrared Physics and Technology, 83, 17-23(2017).
[11] Zhou Feiyan, Jin Linpeng, Dong Jun. Review of convolutional neural network[J]. Chinese Journal of Computers, 40, 1229-1251(2017).
[12] [12] Kang Chaomeng. Cloud detection in domestic highresolution remote sensing image based deep neural wks[D]. Xi ''an: University of Chinese Academy of Sciences (Xi ''an Institute of Optics & Precision Mechanics, Chinese Academy of Sciences), 2018. (in Chinese)
[13] Zhang Anan, Huang Jinying, Ji Shuwei, et al. Bearin fault pattern recognition based on image classification with CNN[J]. Vibration and Impact, 39, 165-171(2020).
[14] Feng Fuzhou, Zhang Chaosheng, Song Aibin, et al. Probability of detection model for fatigue crack in ultrasonicinfrared imaging[J]. Infrared and Laser Engineering, 45, 0304005(2016).
[15] Xue Shan, Zhang Zhen, Lv Qiongying, et al. Image recognition method of anti UAV system based on convolutional neural network[J]. Infrared and Laser Engineering, 49, 20200154(2020).
[16] [16] Zhang Xiangxiang. Reserach on convolutional code decoders based on deep learning under crelated noise[D]. Beijing: Beijing University of Posts Telecommunications, 2019. (in Chinese)
[17] Wu Yunxia, Tian Yimin. A coal-rock recognition method based on max-pooling sparse coding[J]. Chinese Journal of Engineering, 39, 981-987(2017).
[18] Jiao Jinyang, Zhao Ming, Lin Jing, et al. A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes[J]. Knowledge-Based Systems, 160, 237-250(2018).
[19] Zhu Wenbo, Webb Z T, Mao Kaitian, et al. A deep learning approach for process data visualization using t-distributed stochastic neighbor embedding[J]. Industrial & Engineering Chemistry Research, 58, 9564-9575(2019).
[20] [20] Krizhevsky A, Sutskever I, Hinton G E. Image classification with deep convolutional neural wks [C]International Conference on Neural Infmation Processing Systems, 2012: 11061114.
[21] [21] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions [C]2015 IEEE Conference on Computer Vision Pattern Recognition (CVPR). IEEE, 2015: 18.
[22] Liu Li, Sun Liujie, Wang Wenju. Classification of fluorescent images in high-throughput dPRC gene chips based on SVM[J]. Packaging Engineering, 41, 223-229(2020).
Get Citation
Copy Citation Text
Li Lin, Xin Liu, Junzhen Zhu, Fuzhou Feng. Research on vibrothermography detection and recognition method of metal fatigue cracks based on CNN[J]. Infrared and Laser Engineering, 2022, 51(3): 20210227
Category: Image processing
Received: Apr. 6, 2021
Accepted: --
Published Online: Apr. 8, 2022
The Author Email: Fuzhou Feng (fengfuzhou@tsinghua.org.cn)