缺陷检测技术的发展与应用研究综述

李少波; 杨静; 王铮; 朱书德; 杨观赐; 李少波; 杨静; 王铮; 朱书德; 杨观赐

doi:10.16383/j.aas.c180538

[1]

周亮, 王振环, 孙东辰, 穆乃锋.现代精密测量技术现状及发展.仪器仪表学报, 2017, 38(8): 1869-1878 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201708005

Zhou Liang, Wang Zhen-Huan, Sun Dong-Chen, Mu Nai-Feng. Present situation and development of modern precision measurement technology. Chinese Journal of Scientific Instrument, 2017, 38(8): 1869-1878 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201708005

[2]

彭开香, 马亮, 张凯.复杂工业过程质量相关的故障检测与诊断技术综述.自动化学报, 2017, 43(3): 349-365 doi: 10.16383/j.aas.2017.c160427

Peng Kai-Xiang, Ma Liang, Zhang Kai. Review of quality-related fault detection and diagnosis techniques for complex industrial processes. Acta Automatica Sinica, 2017, 43(3): 349-365 doi: 10.16383/j.aas.2017.c160427

[3]

Kumar A. Computer-vision-based fabric defect detection: A survey. IEEE Transactions on Industrial Electronics, 2008, 55(1): 348-363 doi: 10.1109/TIE.1930.896476

[4]

武新军, 张卿, 沈功田.脉冲涡流无损检测技术综述.仪器仪表学报, 2016, 37(8): 1698-1712 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608003

Wu Xin-Jun, Zhang Qing, Shen Gong-Tian. Review on advances in pulsed eddy current nondestructive testing technology. Chinese Journal of Scientific Instrument, 2016, 37(8): 1698-1712 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608003

[5]

Seebauer E G, Noh K W. Trends in semiconductor defect engineering at the nanoscale. Materials Science and Engineering: R: Reports, 2010, 70(3-6): 151-168 doi: 10.1016/j.mser.2010.06.007

[6]

李健, 陈世利, 黄新敬, 曾周末, 靳世久.长输油气管道泄漏监测与准实时检测技术综述.仪器仪表学报, 2016, 37(8): 1747-1760 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608006

Li Jian, Chen Shi-Li, Huang Xin-Jing, Zeng Zhou-Mo, Jin Shi-Jiu. Review of leakage monitoring and quasi real-time detection technologies for long gas & oil pipelines. Chinese Journal of Scientific Instrument, 2016, 37(8): 1747-1760 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608006

[7]

田贵云, 高斌, 高运来, 王平, 王海涛, 石永生.铁路钢轨缺陷伤损巡检与监测技术综述.仪器仪表学报, 2016, 37(8): 1763-1780 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608008

Tian Gui-Yun, Gao Bin, Gao Yun-Lai, Wang Ping, Wang Hai-Tao, Shi Yong-Sheng. Review of railway rail defect non-destructive testing and monitoring. Chinese Journal of Scientific Instrument, 2016, 37(8): 1763-1780 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608008

[8]

Kandpal L M, Park E, Tewari J, Cho B K. Spectroscopic techniques for nondestructive quality inspection of pharmaceutical products: A review. Journal of Biosystems Engineering, 2015, 40(4): 394-408 doi: 10.5307/JBE.2015.40.4.394

[9]

沈功田, 李建, 武新军.承压设备脉冲涡流检测技术研究及应用.机械工程学报, 2017, 53(4): 49-58 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201704007

Shen Gong-Tian, Li Jian, Wu Xin-Jun. Research and application of pulsed eddy current testing technology for pressure equipment. Journal of Mechanical Engineering, 2017, 53(4): 49-58 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201704007

[10]

黄凤英.钢轨表面裂纹涡流检测定量评估方法.中国铁道科学, 2017, 38(2): 28-33 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgtdkx201702005

Huang Feng-Ying. Quantitative evaluation method for eddy current testing of rail surface crack. China Railway Science, 2017, 38(2): 28-33 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgtdkx201702005

[11]

周正干, 孙广开.先进超声检测技术的研究应用进展.机械工程学报, 2017, 53(22): 1-10 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201722001

Zhou Zheng-Gan, Sun Guang-Kai. New progress of the study and application of advanced ultrasonic testing technology. Journal of Mechanical Engineering, 2017, 53(22): 1-10 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201722001

[12]

王兴国, 吴文林, 陈正林, 吴南星. LY12硬铝合金损伤缺陷的空气耦合超声检测.中国机械工程, 2017, 28(21): 2582-2587 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgjxgc201721012

Wang Xing-Guo, Wu Wen-Lin, Chen Zheng-Lin, Wu Nan-Xing. Air-coupling ultrasonic testing of defects in LY12 duralumin alloys. China Mechanical Engineering, 2017, 28(21): 2582-2587 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgjxgc201721012

[13]

鹿文浩, 李亚利, 王生进, 丁晓清.基于部件的三维目标检测算法新进展.自动化学报, 2012, 38(4): 497-506 doi: 10.3724/SP.J.1004.2012.00497

Lu Wen-Hao, Li Ya-Li, Wang Sheng-Jin, Ding Xiao-Qing. Improvements of 3D object detection with part-based models. Acta Automatica Sinica, 2012, 38(4): 497-506 doi: 10.3724/SP.J.1004.2012.00497

[14]

马涛, 孙振国, 陈强.基于几何与纹理特征相融合的磁粉探伤裂纹提取算法.清华大学学报(自然科学版), 2018, 58(1): 50-54 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qhdxxb201801008

Ma Tao, Sun Zhen-Guo, Chen Qiang. Crack detection algorithm for fluorescent magnetic particle inspection based on shape and texture features. Journal of Tsinghua University (Science and Technology), 2018, 58(1): 50-54 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qhdxxb201801008

[15]

黄展鸿, 黄春芳, 张鉴炜, 江大志, 鞠苏.声发射技术在纤维增强复合材料损伤检测和破坏过程分析中的应用研究进展.材料导报, 2018, 32(7): 1122-1128 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cldb201807013

Huang Zhan-Hong, Huang Chun-Fang, Zhang Jian-Wei, Jiang Da-Zhi, Ju Su. Acoustic emission technique for damage detection and failure process determination of fiber-reinforced polymer composites: An application review. Materials Review, 2018, 32(7): 1122-1128 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cldb201807013

[16]

周德强, 潘萌, 常祥, 王华, 曹丕宇.铁磁性构件缺陷的脉冲涡流检测模式研究.仪器仪表学报, 2017, 38(6): 1498-1505 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201706022

Zhou De-Qiang, Pan Meng, Chang Xiang, Wang Hua, Cao Pi-Yu. Research on detection modes of ferromagnetic component defects using pulsed eddy current. Chinese Journal of Scientific Instrument, 2017, 38(6): 1498-1505 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201706022

[17]

沈功田.承压设备无损检测与评价技术发展现状.机械工程学报, 2017, 53(12): 1-12 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201712001

Shen Gong-Tian. Development status of nondestructive testing and evaluation technique for pressure equipment. Journal of Mechanical Engineering, 2017, 53(12): 1-12 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201712001

[18]

邬冠华, 熊鸿建.中国射线检测技术现状及研究进展.仪器仪表学报, 2017, 37(8): 1683-1695 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608001

Wu Guan-Hua, Xiong Hong-Jian. Radiography testing in China. Chinese Journal of Scientific Instrument, 2017, 37(8): 1683-1695 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608001

[19]

谭超, 董峰.多相流过程参数检测技术综述.自动化学报, 2013, 39(11): 1923-1932 doi: 10.3724/SP.J.1004.2013.01923

Tan Chao, Dong Feng. Parameters measurement for multiphase flow process. Acta Automatica Sinica, 2013, 39(11): 1923-1932 doi: 10.3724/SP.J.1004.2013.01923

[20]

苑玮琦, 薛丹.基于机器视觉的隧道衬砌裂缝检测算法综述.仪器仪表学报, 2017, 38(12): 3100-3111 http://www.cnki.com.cn/Article/CJFDTotal-YQXB201712026.htm

Yuan Wei-Qi, Xue Dan. Review of tunnel lining crack detection algorithm based on machine vision. Chinese Journal of Scientific Instrument, 2017, 38(12): 3100-3111 http://www.cnki.com.cn/Article/CJFDTotal-YQXB201712026.htm

[21]

Yang J, Li S B, Gao Z, Wang Z, Liu W. Real-time recognition method for 0.8 cm darning needles and KR22 bearings based on convolution neural networks and data increase. Applied Sciences, 2018, 8(10): Article No. 1857

[22]

Yang G C, Yang J, Sheng W H, Junior F E F, Li S B. Convolutional neural network-based embarrassing situation detection under camera for social robot in smart homes. Sensors, 2018, 18(5): Article No. 1530

[23]

李东民, 李静, 梁大川, 王超.基于多尺度先验深度特征的多目标显著性检测方法.自动化学报, 2019, 45(11): 2058-2070 doi: 10.16383/j.aas.c170154

Li Dong-Min, Li Jing, Liang Da-Chuan, Wang Chao. Multiple salient objects detection using multi-scale prior and deep features. Acta Automatica Sinica, 2019, 45(11): 2058-2070 doi: 10.16383/j.aas.c170154

[24]

张慧, 王坤峰, 王飞跃.深度学习在目标视觉检测中的应用进展与展望.自动化学报, 2017, 43(8): 1289-1305 doi: 10.16383/j.aas.2017.c160822

Zhang Hui, Wang Kun-Feng, Wang Fei-Yue. Advances and perspectives on applications of deep learning in visual object detection. Acta Automatica Sinica, 2017, 43(8): 1289-1305 doi: 10.16383/j.aas.2017.c160822

[25]

Fang F, Li L, Gu Y, et al. A novel hybrid approach for crack detection. Pattern Recognition, 2020, 107: 107474. doi: 10.1016/j.patcog.2020.107474

[26]

Jiang J F, Chen Z M, He K J. A feature-based method of rapidly detecting global exact symmetries in CAD models. Computer-Aided Design, 2013, 45(8-9): 1081-1094 doi: 10.1016/j.cad.2013.04.005

[27]

Cheng J C P, Wang M Z. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques. Automation in Construction, 2018, 95: 155-171 doi: 10.1016/j.autcon.2018.08.006

[28]

Bergmann P, Löwe S, Fauser M, Sattlegger D, Steger C. Improving unsupervised defect segmentation by applying structural similarity to autoencoders. arXiv preprint arXiv: 1807.02011, 2018

[29]

Yang J, Yang G C. Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer. Algorithms, 2018, 11(3): Article No. 28

[30]

Lin J H, Yao Y, Ma L, Wang Y J. Detection of a casting defect tracked by deep convolution neural network. The International Journal of Advanced Manufacturing Technology, 2018, 97(1): 573-581 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2723e5420c66ddd5873b743aea74d134

[31]

Wei F, Yao G, Yang Y, et al. Instance-level recognition and quantification for concrete surface bughole based on deep learning. Automation in Construction, 2019, 107: 102920 doi: 10.1016/j.autcon.2019.102920

[32]

Tao X, Wang Z H, Zhang Z T, Zhang D P, Xu D, Gong X Y, et al. Wire defect recognition of spring-wire socket using multitask convolutional neural networks. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2018, 8(4): 689-698 doi: 10.1109/TCPMT.2018.2794540

[33]

林懿伦, 戴星原, 李力, 王晓, 王飞跃.人工智能研究的新前线:生成式对抗网络.自动化学报, 2018, 44(5): 775-792 doi: 10.16383/j.aas.2018.y000002

Lin Yi-Lun, Dai Xing-Yuan, Li Li, Wang Xiao, Wang Fei-Yue. The new frontier of AI research: Generative adversarial networks. Acta Automatica Sinica, 2018, 44(5): 775-792 doi: 10.16383/j.aas.2018.y000002

[34]

Xue Y D, Li Y C. A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(8): 638-654 doi: 10.1111/mice.12367

[35]

Lei J, Gao X, Feng Z L, Qiu H M, Song M L. Scale insensitive and focus driven mobile screen defect detection in industry. Neurocomputing, 2018, 294: 72-81 doi: 10.1016/j.neucom.2018.03.013

[36]

文立伟, 宋清华, 秦丽华, 肖军.基于机器视觉与UMAC的自动铺丝成型构件缺陷检测闭环控制系统.航空学报, 2015, 36(12): 3991-4000 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hkxb201512023

Wen Li-Wei, Song Qing-Hua, Qin Li-Hua, Xiao Jun. Defect detection and closed-loop control system for automated fiber placement forming components based on machine vision and UMAC. Acta Aeronautica et Astronautica Sinica, 2015, 36(12): 3991-4000 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hkxb201512023

[37]

周德强, 王俊, 张秋菊, 吴静静, 张洪.铁磁性构件缺陷的脉冲涡流检测传感机理研究.仪器仪表学报, 2015, 36(5): 989-995 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201505004

Zhou De-Qiang, Wang Jun, Zhang Qiu-Ju, Wu Jing-Jing, Zhang Hong. Research on sensing mechanism of ferromagnetic component flaw using pulsed eddy current testing. Chinese Journal of Scientific Instrument, 2015, 36(5): 989-995 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201505004

[38]

吴挺, 鲁少辉, 韩旺明, 胡克钢, 汤一平.基于主动式全景视觉传感器的管道内部缺陷检测方法.仪器仪表学报, 2015, 36(10): 2258-2264 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201510013

Wu Ting, Lu Shao-Hui, Han Wang-Ming, Hu Ke-Gang, Tang Yi-Ping. In-pipe internal defect inspection method based on active stereo omni-directional vision sensor. Chinese Journal of Scientific Instrument, 2015, 36(10): 2258-2264 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201510013

[39]

曾伟, 王海涛, 田贵云, 方凌, 汪文, 万敏, 等.基于能量分析的激光超声波缺陷检测研究.仪器仪表学报, 2014, 35(3): 650-655 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201403023

Zeng Wei, Wang Hai-Tao, Tian Gui-Yun, Fang Lin, Wang Wen, Wan Min, et al. Research on laser ultrasonic defect signal detection technology based on energy analysis. Chinese Journal of Scientific Instrument, 2014, 35(3): 650-655 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201403023

[40]

Wilson J W, Tian G Y. Pulsed electromagnetic methods for defect detection and characterisation. NDT & E International, 2007, 40(4): 275-283 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4a0a93e26cddbf2b7ee24310cb943045

[41]

Shi D Q, Gao G L, Xiao P, Gao Z W. Defects detection system for steel tubes based on electromagnetic acoustic technology. Procedia Engineering, 2012, 29: 252-256 doi: 10.1016/j.proeng.2011.12.702

[42]

周德强, 田贵云, 尤丽华, 王海涛, 王平.基于频谱分析的脉冲涡流缺陷检测研究.仪器仪表学报, 2011, 32(9): 1948-1953 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201109005

Zhou De-Qiang, Tian Gui-Yun, You Li-Hua, Wang Hai-Tao, Wang Ping. Study on pulsed eddy current defect signal detection technology based on spectrum analysis. Chinese Journal of Scientific Instrument, 2011, 32(9): 1948-1953 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201109005

[43]

Zhang H, Gao B, Tian G Y, Woo W L, Bai L B. Metal defects sizing and detection under thick coating using microwave NDT. NDT & E International, 2013, 60: 52-61 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9554883e378701839ef44de1ae7c1c7e

[44]

林乃昌, 杨晓翔, 林文剑, 朱志彬.基于抛物线拟合的TOFD图像缺陷检测.焊接学报, 2014, 35(6): 105-108 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb201406025

Lin Nai-Chang, Yang Xiao-Xiang, Lin Wen-Jian, Zhu Zhi-Bin. Defect detection of TOFD D scanning image based on parabola fitting. Transactions of The China Welding Institution, 2014, 35(6): 105-108 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb201406025

[45]

曾伟, 王海涛, 田贵云, 方凌, 汪文.基于Hilbert变换的激光超声波成像技术在缺陷检测中的应用.中国激光, 2014, 41(5): 182-188 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgjg201405033

Zeng Wei, Wang Hai-Tao, Tian Gui-Yun, Fang Ling, Wang Wen. Application laser ultrasound imaging technology for detecting defect based on Hilbert transform. Chinese Journal of Lasers, 2014, 41(5): 182-188 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgjg201405033

[46]

迟大钊, 刚铁.基于超声杂波抑制的缺陷检测.焊接学报, 2015, 36(10): 17-20 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb201510005

Chi Da-Zhao, Gang Tie. Defect detection method based on ultrasonic clutter wave suppression. Transactions of the China Welding Institution, 2015, 36(10): 17-20 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb201510005

[47]

黄刚.基于超声透射时差法的金属棒缺陷检测研究.仪器仪表学报, 2016, 37(4): 818-826 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201604014

Huang Gang. Research on defect detection system for material based on ultrasonic transmission method. Chinese Journal of Scientific Instrument, 2016, 37(4): 818-826 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201604014

[48]

师奕兵, 罗清旺, 王志刚, 张伟, 马东.基于多元接收线圈的管道局部缺陷检测方法研究.电子学报, 2018, 46(1): 197-202 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianzixb201801027

Shi Yi-Bing, Luo Qing-Wang, Wang Zhi-Gang, Zhang Wei, Ma Dong. Research on the detection of local defects of pipes based on dual receivers. Acta Electronica Sinica, 2018, 46(1): 197-202 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianzixb201801027

[49]

屈尔庆, 崔月姣, 徐森, 孙鹤旭.改进的Gabor滤波器带钢表面缺陷显著性检测.华中科技大学学报(自然科学版), 2017, 45(10): 12-17 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9882887

Qu Er-Qing, Cui Yue-Jiao, Xu Sen, Sun He-Xu. Saliency defect detection in strip steel by improved Gabor filter. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2017, 45(10): 12-17 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9882887

[50]

袁小翠, 吴禄慎, 陈华伟.钢轨表面缺陷检测的图像预处理改进算法.计算机辅助设计与图形学学报, 2014, 26(5): 800-805 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjfzsjytxxxb201405017

Yuan Xiao-Cui, Wu Lu-Shen, Chen Hua-Wei. Improved image preprocessing algorithm for rail surface defects detection. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(5): 800-805 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjfzsjytxxxb201405017

[51]

Ge J H, Li W, Chen G M, Yin X K, Yuan X N, Yang W C, et al. Multiple type defect detection in pipe by Helmholtz electromagnetic array probe. NDT & E International, 2017, 91: 97-107 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fe3a2275049970a1ecc26c97fea440ae

[52]

Kazantsev I G, Lemahieu I, Salov G I, Denys R. Statistical detection of defects in radiographic images in nondestructive testing. Signal Processing, 2002, 82(5): 791-801 doi: 10.1016/S0165-1684(02)00158-5

[53]

Tian Y, Du D, Cai G R, Wang L, Zhang H. Automatic defect detection in X-Ray images using image data fusion. Tsinghua Science & Technology, 2006, 11(6): 720-724 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qhdxxb-e200606012

[54]

贺振东, 王耀南, 毛建旭, 印峰.基于反向P-M扩散的钢轨表面缺陷视觉检测.自动化学报, 2014, 40(8): 1667-1679 doi: 10.3724/SP.J.1004.2014.01667

He Zhen-Dong, Wang Yao-Nan, Mao Jian-Xu, Yin Feng. Research on inverse P-M diffusion-based rail surface defect detection. Acta Automatica Sinica, 2014, 40(8): 1667-1679 doi: 10.3724/SP.J.1004.2014.01667

[55]

杨理践, 曹辉.基于深度学习的管道焊缝法兰组件识别方法.仪器仪表学报, 2018, 39(2): 193-202 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201802023

Yang Li-Jian, Cao Hui. Deep learning based weld and flange identification in pipeline. Chinese Journal of Scientific Instrument, 2018, 39(2): 193-202 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201802023

[56]

Duan H F. Transient frequency response based leak detection in water supply pipeline systems with branched and looped junctions. Journal of Hydroinformatics, 2017, 19(1): 17-30 doi: 10.2166/hydro.2016.008

[57]

钱晓亮, 张鹤庆, 张焕龙, 贺振东, 杨存祥.基于视觉显著性的太阳能电池片表面缺陷检测.仪器仪表学报, 2017, 38(7): 1570-1578 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201707002

Qian Xiao-Liang, Zhang He-Qing, Zhang Huan-Long, He Zhen-Dong, Yang Cun-Xiang. Solar cell surface defect detection based on visual saliency. Chinese Journal of Scientific Instrument, 2017, 38(7): 1570-1578 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201707002

[58]

迟大钊, 李孙珏, 孙昌立, 刚铁.基于双目视觉的缺陷深度测量方法.焊接学报, 2016, 37(11): 7-10 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb201611002

Chi Da-Zhao, Li Sun-Jue, Sun Chang-Li, Gang Tie. Binocular visual based defect buried depth testing method. Transactions of the China Welding Institution, 2016, 37(11): 7-10 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb201611002

[59]

Galan U, Orta P, Kurfess T, Ahuett-Garza H. Surface defect identification and measurement for metal castings by vision system. Manufacturing Letters, 2018, 15: 5-8 doi: 10.1016/j.mfglet.2017.12.001

[60]

杨丽君, 田洪刚, 安立明, 温银堂, 罗小元.基于同面电容成像的航天隔热复合材料粘接缺陷检测方法.兵工学报, 2017, 38(12): 2488-2496 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bgxb201712024

Yang Li-Jun, Tian Hong-Gang, An Li-Ming, Wen Yin-Tang, Luo Xiao-Yuan. Bonding defect detection method of aeronautical insulating compsites based on coplanar capacitance imaging reconstruction. Acta Armamentarii, 2017, 38(12): 2488-2496 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bgxb201712024

[61]

朱妍妍, 左建华, 卢继平, 徐东晓.基于图像处理的胶管缺陷在线检测系统开发.北京理工大学学报, 2017, 37(9): 937-941 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bjlgdxxb201709010

Zhu Yan-Yan, Zuo Jian-Hua, Lu Ji-Ping, Xu Dong-Xiao. A on-line detection system development based on image processing for rubber hose defects. Transactions of Beijing Institute of Technology, 2017, 37(9): 937-941 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bjlgdxxb201709010

[62]

林剑春, 杨爱军, 沈熠辉.电致发光缺陷检测仪的成像性能评估.光学精密工程, 2017, 25(6): 1418-1424 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gxjmgc201706003

Lin Jian-Chun, Yang Ai-Jun, Shen Yi-Hui. Evaluation of imaging performance for electroluminescence defect detector. Optics and Precision Engineering, 2017, 25(6): 1418-1424 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gxjmgc201706003

[63]

杨成立, 殷鸣, 向召伟, 范奎.基于非下采样Shearlet变换的磁瓦表面缺陷检测.工程科学与技术, 2017, 49(2): 217-224 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=scdxxb-gckx201702028

Yang Cheng-Li, Yin Ming, Xiang Zhao-Wei, Fan Kui. Defect detection in magnetic tile images based on non-subsampled Shearlet transform. Advanced Engineering Sciences, 2017, 49(2): 217-224 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=scdxxb-gckx201702028

[64]

周秀云, 薛云, 周金龙.基于多物理场的焊球缺陷检测方法.西南交通大学学报, 2017, 52(2): 363-368 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xnjtdxxb201702021

Zhou Xiu-Yun, Xue Yun, Zhou Jin-Long. Defect detection of solder balls based on multi-physical field. Journal of Southwest Jiaotong University, 2017, 52(2): 363-368 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xnjtdxxb201702021

[65]

Fieres J, Schumann P, Reinhart C. Predicting failure in additively manufactured parts using X-ray computed tomography and simulation. Procedia Engineering, 2018, 213: 69-78 doi: 10.1016/j.proeng.2018.02.008

[66]

Chehami L, Moulin E, de Rosny J, Prada C, Chatelet E, Lacerra G, et al. Nonlinear secondary noise sources for passive defect detection using ultrasound sensors. Journal of Sound & Vibration, 2017, 386: 283-294 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1930200db3c2a6b805806ad2147c276b

[67]

Droubi M G, Faisal N H, Orr F, Steel J A, El-Shaib M. Acoustic emission method for defect detection and identification in carbon steel welded joints. Journal of Constructional Steel Research, 2017, 134: 28-37 doi: 10.1016/j.jcsr.2017.03.012

[68]

D'Angelo G, Rampone S. Feature extraction and soft computing methods for aerospace structure defect classification. Measurement, 2016, 85: 192-209 doi: 10.1016/j.measurement.2016.02.027

[69]

Pichler K, Lughofer E, Pichler M, Buchegger T, Klement E P, Huschenbett M. Fault detection in reciprocating compressor valves under varying load conditions. Mechanical Systems and Signal Processing, 2016, 70-71: 104-119 doi: 10.1016/j.ymssp.2015.09.005

[70]

余永维, 杜柳青, 曾翠兰, 张建恒.基于深度学习特征匹配的铸件微小缺陷自动定位方法.仪器仪表学报, 2016, 37(6): 1364-1370 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201606021

Yu Yong-Wei, Du Liu-Qing, Zeng Cui-Lan, Zhang Jian-Heng. Automatic localization method of small casting defect based on deep learning feature. Chinese Journal of Scientific Instrument, 2016, 37(6): 1364-1370 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201606021

[71]

孙建平, 王逢瑚, 朱晓冬.小波--神经网络在MDF缺陷定位检测中的应用.仪器仪表学报, 2008, 29(5): 954-958 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb200805011

Sun Jian-Ping, Wang Feng-Hu, Zhu Xiao-Dong. Application of wavelet-neural network in defect location non-destructive testing of MDF. Chinese Journal of Scientific Instrument, 2008, 29(5): 954-958 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb200805011

[72]

Park J K, Kwon B K, Park J H, Kang D J. Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing-Green Technology, 2016, 3(3): 303-310 doi: 10.1007/s40684-016-0039-x

[73]

Chen J W, Liu Z G, Wang H R, Núñez A, Han Z W. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Transactions on Instrumentation & Measurement, 2018, 67(2): 257-269 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=70e333fb479671d31e97aa2799987209

[74]

Napoletano P, Piccoli F, Schettini R. Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors, 2018, 18(1): Article No. 209

[75]

Yang Z Y, Lu S H, Wu T, Yuan G P, Tang Y P. Detection of morphology defects in pipeline based on 3D active stereo omnidirectional vision sensor. IET Image Processing, 2018, 12(4): 588-595 doi: 10.1049/iet-ipr.2017.0616

[76]

Yuan Z C, Zhang Z T, Su H, Zhang L, Shen F, Zhang F. Vision-based defect detection for mobile phone cover glass using deep neural networks. International Journal of Precision Engineering and Manufacturing, 2018, 19(6): 801-810 doi: 10.1007/s12541-018-0096-x

[77]

Liu R, Gu Q, Wang X, Yao M. Region-convolutional neural network for detecting capsule surface defects. Boletin Tecnico/Technical Bulletin, 2017, 55(3): 92-100

[78]

Krummenacher G, Ong C S, Koller S, Kobayashi S, Buhmann J M. Wheel defect detection with machine learning. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(4): 1176-1187 doi: 10.1109/TITS.2017.2720721

[79]

Kumar S S, Abraham D M, Jahanshahi M R, Iseley T, Starr J. Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks. Automation in Construction, 2018, 91: 273-283 doi: 10.1016/j.autcon.2018.03.028

[80]

Shaw D, Al-Khalili D, Rozon C. Fault security analysis of CMOS VLSI circuits using defect-injectable VHDL models. Integration, 2002, 32(1-2): 77-97 doi: 10.1016/S0167-9260(02)00043-3

[81]

林晓玲, 恩云飞, 姚若河. 3D叠层封装集成电路的缺陷定位方法.华南理工大学学报(自然科学版), 2016, 44(5): 36-41, 47 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hnlgdxxb201605006

Lin Xiao-Ling, En Yun-Fei, Yao Ruo-He. Defect localization method of 3D stacked-die packaged integrated circuits. Journal of South China University of Technology (Natural Science Edition), 2016, 44(5): 36-41, 47 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hnlgdxxb201605006

[82]

Wu C L, Yao S Y, Corinne B. Leakage current study and relevant defect localization in integrated circuit failure analysis. Microelectronics Reliability, 2015, 55(3-4): 463-469 doi: 10.1016/j.microrel.2015.01.005

[83]

Chen X L, Liu L Y, Li E L. Metal defect localization of GaAs or Si based ICs by dynamic emission microscopy. Microelectronics Reliability, 2017, 72: 24-29 doi: 10.1016/j.microrel.2017.03.003

[84]

Chao L C, Tong L I. Wafer defect pattern recognition by multi-class support vector machines by using a novel defect cluster index. Expert Systems with Applications, 2009, 36(6): 10158-10167 doi: 10.1016/j.eswa.2009.01.003

[85]

Bouwens M A J, Maas D J, van der Donck J C J, Alkemade P F A, van der Walle P. Enhancing re-detection efficacy of defects on blank wafers using stealth fiducial markers. Microelectronic Engineering, 2016, 153: 48-54 doi: 10.1016/j.mee.2016.01.007

[86]

周启忠, 谢永乐.模拟集成电路故障诊断与参数辨识的代数方法.四川大学学报(工程科学版), 2016, 48(4): 158-166 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=scdxxb-gckx201604022

Zhou Qi-Zhong, Xie Yong-Le. Algebraic methodology on fault diagnosis and parametric identification for analog integrated circuits. Journal of Sichuan University (Engineering Science Edition), 2016, 48(4): 158-166 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=scdxxb-gckx201604022

[87]

Wang J P, Wu Y, Zhao T W. Short critical area model and extraction algorithm based on defect characteristics in integrated circuits. Analog Integrated Circuits and Signal Processing, 2017, 91(1): 83-91 doi: 10.1007/s10470-016-0841-y

[88]

Jung H K, Lee C W, Park G. Fast and non-invasive surface crack detection of press panels using image processing. Procedia Engineering, 2017, 188: 72-79 doi: 10.1016/j.proeng.2017.04.459

[89]

Shi Q Z, Liu J Y, Wang Y, Liu W Y. Study on the detection of CFRP material with subsurface defects using barker-coded thermal wave imaging (BC-TWI) as a nondestructive inspection (NDI) tool. International Journal of Thermophysics, 2018, 39(8): Article No. 92

[90]

Hartmann C, Wieberneit M. Investigation on BIST assisted failure analysis on digital integrated circuits. Microelectronics Reliability, 2010, 50(9-11): 1464-1468 doi: 10.1016/j.microrel.2010.07.148

[91]

Roesch W J, Hamada D J M. Discovering and reducing defects in MIM capacitors. Microelectronics Reliability, 2018, 81: 299-305 doi: 10.1016/j.microrel.2017.10.021

[92]

黄松岭, 王哲, 王珅, 赵伟.管道电磁超声导波技术及其应用研究进展.仪器仪表学报, 2018, 39(3): 1-12 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201803001

Huang Song-Ling, Wang Zhe, Wang Kun, Zhao Wei. Review on advances of pipe electromagnetic ultrasonic guided waves technology and its application. Chinese Journal of Scientific Instrument, 2018, 39(3): 1-12 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201803001

[93]

雷小军, 付庄, 曹其新, 赵言正.海底管道检测机器人自主缺陷定位的模糊控制研究.机器人, 2005, 27(3): 252-255 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jqr200503012

Lei Xiao-Jun, Fu Zhuang, Cao Qi-Xin, Zhao Yan-Zheng. Fuzzy control of autonomous defect location for submarine in-pipeline inspection robots. Robot, 2005, 27(3): 252-255 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jqr200503012

[94]

Grin E A, Bochkarev V I. Estimate of the allowable dimensions of diagnosed defects in category Ⅲ and IV welded pipeline joints. Power Technology & Engineering, 2013, 46(5): 394-398

[95]

刘素贞, 张严伟, 张闯, 金亮, 杨庆新.电磁超声管道周向兰姆波仿真分析及缺陷检测特性研究.电工技术学报, 2017, 32(22): 144-151 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dgjsxb201722016

Liu Su-Zhen, Zhang Yan-Wei, Zhang Chuang, Jin Liang, Yang Qing-Xin. Research on simulation analysis of electromagnetic ultrasonic circumferential lamb waves and defect feature detection in pipeline. Transactions of China Electrotechnical Society, 2017, 32(22): 144-151 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dgjsxb201722016

[96]

Song Q, Ding W X, Peng H, Gu J J, Shuai J. Pipe defect detection with remote magnetic inspection and wavelet analysis. Wireless Personal Communications, 2017, 95(3): 2299-2313 doi: 10.1007/s11277-017-4092-8

[97]

Mao B Y, Lu Y, Wu P L, Mao B Z, Li P F. Signal processing and defect analysis of pipeline inspection applying magnetic flux leakage methods. Intelligent Service Robotics, 2014, 7(4): 203-209 doi: 10.1007/s11370-014-0158-6

[98]

武静, 张伟伟, 聂振华, 马宏伟, 杨飞.基于Lyapunov指数的管道超声导波小缺陷定位实验研究.振动与冲击, 2016, 35(1): 40-45, 53 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdycj201601008

Wu Jing, Zhang Wei-Wei, Nie Zhen-Hua, Ma Hong-Wei, Yang Fei. Tests for datecting crack locations in a pipe with ultrasonic guided wave based on Lyapunov exponent. Journal of Vibration and Shock, 2016, 35(1): 40-45, 53 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdycj201601008

[99]

周进节, 郑阳, 杨齐, 张宗健.管道超声导波分段时间反转检测方法研究.机械工程学报, 2017, 53(12): 78-86 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201712010

Zhou Jin-Jie, Zheng Yang, Yang Qi, Zhang Zong-Jian. Pipeline section time reversal inspection method with ultrasonic guided waves. Journal of Mechanical Engineering, 2017, 53(12): 78-86 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201712010

[100]

何存富, 邓鹏, 吕炎, 焦敬品, 吴斌.一种高信噪比电磁声表面波传感器及在厚壁管道检测中的应用.机械工程学报, 2017, 53(4): 59-66 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201704008

He Cun-Fu, Deng Peng, Lv Yan, Jiao Jing-Pin, Wu Bin. A new surface wave EMAT with high SNR and the application for defect detection in thick-walled pipes. Journal of Mechanical Engineering, 2017, 53(4): 59-66 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201704008

[101]

Oh S W, Yoon D B, Kim G J, Bae J H, Kim H S. Acoustic data condensation to enhance pipeline leak detection. Nuclear Engineering and Design, 2018, 327: 198-211 doi: 10.1016/j.nucengdes.2017.12.006

[102]

Duan H F. Accuracy and sensitivity evaluation of TFR method for leak detection in multiple-pipeline water supply systems. Water Resources Management, 2018, 32(6): 2147-2164 doi: 10.1007/s11269-018-1923-7

[103]

何存富, 郑明方, 吕炎, 邓鹏, 赵华民, 刘秀成, 等.超声导波检测技术的发展、应用与挑战.仪器仪表学报, 2016, 37(8): 1713-1735 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608004

He Cun-Fu, Zheng Ming-Fang, Lv Yan, Deng Peng, Zhao Hua-Ming, Liu Xiu-Cheng, et al. Development, applications and challenges in ultrasonic guided waves testing technology. Chinese Journal of Scientific Instrument, 2016, 37(8): 1713-1735 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201608004

[104]

石端虎, 刚铁, 杨根喜, 黄传辉.工字形激光焊件中批量缺陷定位数据的自动提取.焊接学报, 2009, 30(10): 49-52 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb200910013

Shi Duan-Hu, Gang Tie, Yang Gen-Xi, Huang Chuan-Hui. Automatic extraction of locating data for bulk defects in I-shaped laser weldments. Transactions of the China Welding Institution, 2009, 30(10): 49-52 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hjxb200910013

[105]

Rodil S S, Gómez R A, Bernárdez J M, Rodríguez F, Miguel L J, Perán J R. Laser welding defects detection in automotive industry based on radiation and spectroscopical measurements. International Journal of Advanced Manufacturing Technology, 2010, 49(1-4): 133-145 doi: 10.1007/s00170-009-2395-y

[106]

Lindgren E. Detection and 3-D positioning of small defects using 3-D point reconstruction, tracking, and the radiographic magnification technique. NDT & E International, 2015, 76: 1-8

[107]

Makhutov N A, Ushakov B N, Vasilév I E. Strength assessment and defect detection in welded pipeline seams by means of brittle tensosensitive coatings. Russian Engineering Research, 2011, 31(2): 123-127 doi: 10.3103/S1068798X1102016X

[108]

Zhang Z F, Kannatey-Asibu E Jr, Chen S B, Huang Y M, Xu Y L. Online defect detection of Al alloy in arc welding based on feature extraction of arc spectroscopy signal. The International Journal of Advanced Manufacturing Technology, 2015, 79(9-12): 2067-2077 doi: 10.1007/s00170-015-6966-9

[109]

Kim H M, Choi D H. Defects detection of gas pipeline near the welds based on self quotient image and discrete cosine transform. Russian Journal of Nondestructive Testing. 2016, 52(3): 175-183 doi: 10.1134/S1061830916030049

[110]

Mirapeix J, Ruiz-Lombera R, Valdiande J J, Rodriguez-Cobo L, Anabitarte F, Cobo A. Defect detection with CCD-spectrometer and photodiode-based arc-welding monitoring systems. Journal of Materials Processing Technology, 2011, 211(12): 2132-2139 doi: 10.1016/j.jmatprotec.2011.07.011

[111]

Liu J, Xu G C, Ren L, Qian Z H, Ren L Q. Defect intelligent identification in resistance spot welding ultrasonic detection based on wavelet packet and neural network. The International Journal of Advanced Manufacturing Technology, 2017, 90(9-12): 2581-2588 doi: 10.1007/s00170-016-9588-y

[112]

Chu H H, Wang Z Y. A vision-based system for post-welding quality measurement and defect detection. The International Journal of Advanced Manufacturing Technology, 2016, 86(9): 3007-3014 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6830d08f8d7465139bab80cb03b45d61

[113]

Malarvel M, Sethumadhavan G, Bhagi P C R, Kar S, Saravanan T, Krishnan A. Anisotropic diffusion based denoising on X-radiography images to detect weld defects. Digital Signal Processing, 2017, 68: 112-126 doi: 10.1016/j.dsp.2017.05.014

[114]

Guo Z Y, Ye S F, Wang Y J, Lin C. Resistance Welding spot defect detection with convolutional neural networks. In: Proceedings of the 2017 International Conference on Computer Vision Systems. Cham: Springer, 2017. 169-174

[115]

Ye G L, Guo J W, Sun Z Z, Li C, Zhong S Y. Weld bead recognition using laser vision with model-based classification. Robotics and Computer-Integrated Manufacturing, 2018, 52: 9-16 doi: 10.1016/j.rcim.2018.01.006

[116]

温银堂, 赵丽梅, 张玉燕, 潘钊, 王洪瑞.基于ECT的复合材料构件胶层缺陷检测.仪器仪表学报, 2015, 36(8): 1783-1791 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201508013

Wen Yin-Tang, Zhao Li-Mei, Zhang Yu-Yan, Pan Zhao, Wang Hong-Rui. Defect detection of the adhesive layer of composite component based on the ECT technology. Chinese Journal of Scientific Instrument, 2015, 36(8): 1783-1791 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201508013

[117]

Jing T, Zhang S B, Shi X D, Wang L W. Design of aircraft cable fault diagnose and location system based on aircraft airworthiness requirement. Procedia Engineering, 2011, 17: 455-464 doi: 10.1016/j.proeng.2011.10.055

[118]

Mazlumi F, Gharanfeli N, Sadeghi S H H, Moini R. An open-ended substrate integrated waveguide probe for detection and sizing of surface cracks in metals. NDT & E International, 2013, 53: 36-38 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b00d35bd4f1157b9e39b1ea55eda0c67

[119]

Yang Y Y, Chai H T, Li C, Zhang Y H, Wu F, Bai J, et al. Surface defects evaluation system based on electromagnetic model simulation and inverse-recognition calibration method. Optics Communications, 2017, 390: 88-98 doi: 10.1016/j.optcom.2016.12.075

[120]

Zhang J, Drinkwater B W, Wilcox P D, et al. Defect detection using ultrasonic arrays: The multi-mode total focusing method. NDT & E International, 2010, 43(2): 123-133

[121]

Ghose B, Kankane D K. Estimation of location of defects in propellant grain by X-ray radiography. NDT & E International, 2008, 41(2): 125-128

[122]

Yan Z B, Chen C Y, Luo L K, Yao Y. Stable principal component pursuit-based thermographic data analysis for defect detection in polymer composites. Journal of Process Control, 2017, 49: 36-44 doi: 10.1016/j.jprocont.2016.11.008

[123]

Benmoussat M S, Guillaume M, Caulier Y, Spinnler K. Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms. Infrared Physics & Technology, 2013, 61: 68-80

[124]

Zheng K Y, Chang Y S, Wang K H, Yao Y. Thermographic clustering analysis for defect detection in CFRP structures. Polymer Testing, 2016, 49: 73-81 doi: 10.1016/j.polymertesting.2015.11.009

[125]

Ghidoni S, Antonello M, Nanni L, Menegatti E. A thermographic visual inspection system for crack detection in metal parts exploiting a robotic workcell. Robotics and Autonomous Systems, 2015, 74: 351-359 doi: 10.1016/j.robot.2015.07.020

[126]

Holzmond O, Li X D. In situ real time defect detection of 3D printed parts. Additive Manufacturing, 2017, 17: 135-142 doi: 10.1016/j.addma.2017.08.003

[127]

宋伟, 左丹, 邓邦飞, 张海兵, 薛凯文, 胡泓.高压输电线防震锤锈蚀缺陷检测.仪器仪表学报, 2016, 37(S1): 113-117 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9580938

Song Wei, Zuo Dan, Deng Bang-Fei, Zhang Hai-Bing, Xue Kai-Wen, Hu Hong. Corrosion defect detection of earthquake hammer for high voltage transmission line. Chinese Journal of Scientific Instrument, 2016, 37(S1): 113-117 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9580938

[128]

Molina J, Solanes J E, Arnal L, Tornero J. On the detection of defects on specular car body surfaces. Robotics and Computer-Integrated Manufacturing, 2017, 48: 263-278 doi: 10.1016/j.rcim.2017.04.009

[129]

Meng F, Ren J, Wang Q, et al. Rubber hose surface defect detection system based on machine vision. In: Proceedings of the 2018 IOP Conference Series: Earth and Environmental Science. 2018, 108(2): 022057

[130]

沈凌云, 朱明, 陈小云.基于径向基神经网络的太阳能电池缺陷检测.发光学报, 2015, 36(1): 99-105 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fgxb201501015

Shen Ling-Yun, Zhu Ming, Chen Xiao-Yun. Solar panels defect detection based on radial basis function neural network. Chinese Journal of Luminescence, 2015, 36(1): 99-105 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fgxb201501015

[131]

Woźniak M, Polap D. Adaptive neuro-heuristic hybrid model for fruit peel defects detection. Neural Networks, 2018, 98: 16-33 doi: 10.1016/j.neunet.2017.10.009

[132]

Gupta R K, Gurumoorthy B. Classification, representation, and automatic extraction of deformation features in sheet metal parts. Computer-Aided Design, 2013, 45(11): 1469-1484 doi: 10.1016/j.cad.2013.06.010

[133]

侯忠生, 许建新.数据驱动控制理论及方法的回顾和展望.自动化学报, 2009, 35(6): 650-667 doi: 10.3724/SP.J.1004.2009.00650

Hou Zhong-Sheng, Xu Jian-Xin. On data-driven control theory: The state of the art and perspective. Acta Automatica Sinica, 2009, 35(6): 650-667 doi: 10.3724/SP.J.1004.2009.00650

[134]

Behera A K, Lauwers B, Duflou J R. Advanced feature detection algorithms for incrementally formed sheet metal parts. Transactions of Nonferrous Metals Society of China, 2012, 22(S2): S315-S322

[135]

Tao X, Xu D, Zhang Z T, Zhang F, Liu X L, Zhang D P. Weak scratch detection and defect classification methods for a large-aperture optical element. Optics Communications, 2017, 387: 390-400 doi: 10.1016/j.optcom.2016.10.062

[136]

Martínez-Rego D, Fontenla-Romero O, Alonso-Betanzos A, Principe J C. Fault detection via recurrence time statistics and one-class classification. Pattern Recognition Letters, 2016, 84: 8-14 doi: 10.1016/j.patrec.2016.07.019

[137]

Shanmugamani R, Sadique M, Ramamoorthy B. Detection and classification of surface defects of gun barrels using computer vision and machine learning. Measurement, 2015, 60: 222-230 doi: 10.1016/j.measurement.2014.10.009

[138]

Hanzaei S H, Afshar A, Barazandeh F. Automatic detection and classification of the ceramic tiles' surface defects. Pattern Recognition, 2017, 66: 174-189 doi: 10.1016/j.patcog.2016.11.021

[139]

Greska W, Franke V, Geiger M. Classification problems in manufacturing of sheet metal parts. Computers in Industry, 1997, 33(1): 17-30 doi: 10.1016/S0166-3615(97)00008-0

[140]

Martínez S S, Vázquez C O, García J G, Ortega J G. Quality inspection of machined metal parts using an image fusion technique. Measurement, 2017, 111: 374-383 doi: 10.1016/j.measurement.2017.08.002

[141]

Cui X D, Goel V, Kingsbury B. Data augmentation for deep neural network acoustic modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015, 23(9): 1469-1477 doi: 10.1109/TASLP.2015.2438544

[142]

Chen X F, Wang S B, Qiao B J, Chen Q. Basic research on machinery fault diagnostics: Past, present, and future trends. Frontiers of Mechanical Engineering, 2018, 13(2): 264-291 doi: 10.1007/s11465-018-0472-3

[143]

Shin H J, Eom D H, Kim S S. One-class support vector machines-an application in machine fault detection and classification. Computers & Industrial Engineering, 2005, 48(2): 395-408 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ed7cafea4b09c1fa0b0b045ee529aa5c

[144]

Chauhan V, Surgenor B. A comparative study of machine vision based methods for fault detection in an automated assembly machine. Procedia Manufacturing, 2015, 1: 416-428 doi: 10.1016/j.promfg.2015.09.051

[145]

Gketsis Z E, Zervakis M E, Stavrakakis G. Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN. Electric Power Systems Research, 2009, 79(11): 1483-1494 doi: 10.1016/j.epsr.2009.05.001

[146]

Zarei J, Tajeddini M A, Karimi H R. Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 2014, 24(2): 151-157 doi: 10.1016/j.mechatronics.2014.01.003

[147]

Guo X, Chen L, Shen C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, 93: 490-502 doi: 10.1016/j.measurement.2016.07.054

[148]

Ruiz M, Mujica L E, Alférez S, Acho L, Tutivén C, Vidal Y, et al. Wind turbine fault detection and classification by means of image texture analysis. Mechanical Systems and Signal Processing, 2018, 107: 149-167 doi: 10.1016/j.ymssp.2017.12.035

[149]

Prytz R, Nowaczyk S, Rögnvaldsson T, et al. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering Applications of Artificial Intelligence, 2015, 41: 139-150. doi: 10.1016/j.engappai.2015.02.009

[150]

Liu J, Li Y F, Zio E. A SVM framework for fault detection of the braking system in a high speed train. Mechanical Systems and Signal Processing, 2017, 87: 401-409 doi: 10.1016/j.ymssp.2016.10.034

[151]

Kuo C F J, Lai C Y, Kao C H, Chiu C H. Integrating image processing and classification technology into automated polarizing film defect inspection. Optics and Lasers in Engineering, 2018, 104: 204-219 doi: 10.1016/j.optlaseng.2017.09.017

[152]

Zhou Y M, Wu K, Meng Z J, Tian M J. Fault detection of aircraft based on support vector domain description. Computers & Electrical Engineering, 2017, 61: 80-94 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=41bc4d9c416c7d57bf40db501561f67a

[153]

Ding J M. Fault detection of a wheelset bearing in a high-speed train using the shock-response convolutional sparse-coding technique. Measurement, 2018, 117: 108-124 doi: 10.1016/j.measurement.2017.12.010

[154]

Hameed Z, Hong Y S, Cho Y M, Ahn S H, Song C K. Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, 2009, 13(1): 1-39

[155]

Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848 doi: 10.1109/TPAMI.2017.2699184

[156]

He K M, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 2980-2988

[157]

Ren S Q, He K M, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149

[158]

Redmon J, Farhadi A. YOLO9000: Better, faster, stronger. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2017. 6517-6525

[159]

Fu C Y, Liu W, Ranga A, Tyagi A, Berg A C. DSSD: Deconvolutional single shot detector. arXiv preprint arXiv: 1701.06659, 2017

[160]

Shen Z Q, Liu Z, Li J G, Jiang Y G, Chen Y R, Xue X Y. DSOD: Learning deeply supervised object detectors from scratch. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 1937-1945

[161]

Lin T Y, Dollár P, Girshick R B, He K M, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017. 936-944

[162]

Lin T Y, Goyal P, Girshick R, He K M, Doll´ar P. Focal loss for dense object detection. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 2999−3007

[163]

Ouyang W L, Wang K, Zhu X, Wang X G. Chained cascade network for object detection. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 1956−1964

[164]

Bodla N, Singh B, Chellappa R, Davis L S. Soft-NMSimproving object detection with one line of code. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 5562−5570

[165]

Hu H, Gu J Y, Zhang Z, Dai J F, Wei Y C. Relation networks for object detection. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018. 3588−3597