Advances and Prospects in Inner Surface Defect Detection Based on Cite Space
SHENG Qiang1, 2, ZHENG Jian-ming1*, LIU Jiang-shan2, SHI Wei-chao1, LI Hai-tao2
1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
2. College of Mechanical & Electrical Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
Abstract:In order to analyze the development, trend and dynamics of inner surface defect detection, 4 708 relevant literature in English and 818 in Chinese were collected through the search of relevant literature in this field in WoS and CNKI databases. The visual analysis software CiteSpace is used to study the knowledge map of literature co-occurrence and clustering, analyze the distribution status and cooperation of internal surface defect detection in countries, institutions and scholars, and sort out the research hotspots and cutting-edge trends. It is found that the research on inner surface defect detection has obvious interdisciplinary attributes, mainly involving analytical chemistry, material science, spectroscopy, instrumentation, mechanical engineering and computer science. In recent years, the annual growth rate of related literature in the WoS database has been more than 10%, and the annual growth rate of CNKI has been more than 20%. China and the United States have become the most active countries in this field, accounting for about 40% of the total number of publications. Chinese scholars’ research in non-destructive testing, image processing and other fields lags behind that of foreign scholars, but they catch up in machine vision and deep learning. According to the research route, it can be divided into detection based on acousto-optic electrothermal magnetism and detection based on the visual imaging. The former includes the acquisition of spectral, ultrasonic and electromagnetic images by different technical means and the realization of defect detection by image processing technology, while the latter is the main defects recognition and classification based on visual image, has become the main research focus in the field. The development of inner surface defect detection can be divided into three stages: defect identification, defect classification and defect analysis. Before 2000 defects were recognized and determined mainly by thermal, acoustic, optic, electrothermal, and magnetic signals or images. Since 2000, the support vector machine (SVM) technology greatly improves the efficiency and accuracy of defect classification. In recent ten years, with the increasing demand for defect analysis and measurement, defect location and measurement based on machine vision has gradually become a development trend, and the object of defect detection has gradually developed to the inner surface of deep holes and small holes.
盛 强,郑建明,刘江山,史卫朝,李海涛. 基于CiteSpace的内表面缺陷检测研究进展与趋势[J]. 光谱学与光谱分析, 2023, 43(01): 9-15.
SHENG Qiang, ZHENG Jian-ming, LIU Jiang-shan, SHI Wei-chao, LI Hai-tao. Advances and Prospects in Inner Surface Defect Detection Based on Cite Space. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 9-15.
[1] YANG Chuan-li, ZHANG Xiu-qing(杨传礼, 张修庆). Materials Reports(材料导报), 2022, (16): 1.
[2] Iakovidis Dimitris K, Maroulis Dimitris E, Karkanis Stavros A. Computers in Biology and Medicine, 2006, 36(10): 1084.
[3] Li Baopu,Meng Max Q H. Image and Vision Computing, 2009, 27(9): 1336.
[4] LIANG Hao, CAO Jun, LIN Xue, et al(梁 浩, 曹 军, 林 雪, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(7): 2041.
[5] Iyer S, Sinha S K. Image and Vision Computing, 2005, 23(10): 921.
[6] Chen Chaomei. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359.
[7] Greenfield F L, Greenfield S F. Transactions of the New York Academy of Sciences, 1964, 26: 453.
[8] Lange P E,Seiffert P A,Pries F,et al. The American Journal of Cardiology, 1985, 55(1): 152.
[9] Rosencwaig A, Opsal J. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 1986, 33(5): 516.
[10] Weaver J B, Xu Y S, Healy D M Jr, et al. Magnetic Resonance in Medicine, 1991, 21(2): 288.
[11] Kubota J, Musha Y, Takahashi M. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 1992, 39(1): 122.
[12] MEI An-hua, WANG Jing-hui(梅安华, 王菁蕙). Journal of Wuhan University of Surveying and Mapping Science and Technology(武汉测绘科技大学学报), 1993,(1): 86.
[13] WU Zhang-jiang, LI Xiang-min(吴章江, 李湘敏). China Railway Science(中国铁道科学), 1993,(1): 36.
[14] LIN Zhi-yue, YUCHI Hao-yi, JIANG Chong-ji(林治钺,尉迟颢颐,江崇吉). Journal of Dalian University of Technology(大连理工大学学报), 1991, (1): 107.
[15] Wakabayashi T, Nakazawa S, Yoshino J, et al. The Japanese Journal of Gastro-Enterology, 1990, 87(7): 1491.
[16] Kranenberg C F, Jungling K C. Applied Optics, 1994, 33(19): 4248.
[17] MA Hong, PAN Yu-xue, BAI Bao-xing, et al(马 宏, 潘毓学, 白宝兴, 等). Acta Armamentarii(兵工学报),1996,(2): 97.
[18] MA Hong, BAI Bao-xing, SHEN Yu-zhi, et al(马 宏, 白宝兴, 沈育志, 等). Chinese Journal of Scientific Instrument(仪器仪表学报), 1996,(2): 166.
[19] Chapelle O, Haffner P, Vapnik V N. IEEE Transactions on Neural Networks, 1999, 10(5): 1055.
[20] Kodogiannis V S, Boulougoura M, Wadge E, et al. Engineering Applications of Artificial Intelligence, 2007, 20(4): 539.
[21] Murosaki T, Yoshida K, Naganuma H. Proceedings of SICE Annual Conference, 2008,1-7: 557.
[22] Suykens J K, Van Gestel T, Vandewalle J, et al. IEEE Transactions on Neural Networks, 2003, 14(2): 447.
[23] Yu Xiao, Lu Yuhua, Gao Qiang. International Journal of Pressure Vessels and Piping, 2021, 189: 104249.
[24] Xie Xiaoming, Zhang Fan, Zeng Yong, et al. 10th International Conference on Digital Image Processing (ICDIP), 2018, 91: 273.
[25] Suresh S, Sundararajan N, Saratchandran P. Neurocomputing, 2008, 71(7-9): 1345.
[26] Safari S, Shoorehdeli M A. Journal of Pipeline System Engineering and Practice, 2018, 9(2): 05018001.
[27] Ji Fengzhu, Wang Changlong, Zuo Xiaozhang, et al. Insight, 2007, 49(9): 516.
[28] Yokota M, Adachi T. Applied Optics, 2011, 50(21): 3937.
[29] Peiner E, Balke M, Doering L. IEEE Sensors Journal, 2008, 8(11-12): 1960.
[30] Gao Wang, Zhang Jin. International Conference on Intelligent Systems Research and Mechatronics Engineering (ISRME), 2015, 121: 608.
[31] Elfurjani S, Ko J, Jun M B G. Measurement, 2016, 89: 215.
[32] Lin Chunfu, Lin Shangfuu, Hwang Chihuang, et al. IEEE Transactions on Instrumentation and Measurement, 2019, 68(8): 2830.
[33] Liu Wei, Zheng Xiuyan, Liu Shuangjun, et al. Journal of Circuits System and Computers, 2012, 21(1): 1250005.
[34] Zhu Rongsheng, Zhang Rong, Xue Dixiu. 8th International Congress on Image and Signal Processing (CISP), 2015. 372.
[35] NIU Qun-yao, YE Ming, LU Yong-hua(牛群遥, 叶 明, 陆永华). Journal of Computer Applications(计算机应用), 2016, 36(10): 2912.
[36] Kumar Srinath S, Abraham Dulcy M, Jahanshahi Mohammad R, et al. Automation in Construction, 2018, 91: 273.
[37] Xiao Zhiguo, Feng Linian. IEEE Access, 2020, 8: 159017.
[38] XIE Xue-jiao, LU Feng, LI Shu-zhan, et al(谢雪娇, 陆 枫, 李书展, 等). Computer Engineering & Science(计算机工程与科学), 2020, 42(10): 1827.
[39] Mo Xi, Tao Ke, Wang Quan, et al. 24th International Conference on Pattern Recognition (ICPR), 2018, 3929.
[40] Chen Yongbin, Fu Qinshen, Wang Guitang. Mobile Information System, 2021: 9374465.
[41] LI Bin, WANG Cheng, WU Jing, et al(李 彬, 汪 诚, 吴 静, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2021, 58(14): 1415004.
[42] JIN Ying, WANG Xue-ying, DUAN Lin-mao(金 颖, 王学影, 段林茂). Modern Manufacturing Engineering(现代制造工程),2020,(5): 125.