Application of Improved Secondary 1.5-D Spectrum Estimation in Pipeline Inner Inspection
TANG Jian1,2,JIAO Xiangdong2,DAI Bo2
(1. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China; 2. Beijing Institute of Petrochemical Technology, Beijing 102617, China)
Abstract： Aimed at the problem that the manual wall thickness acquisition algorithm has the disadvantages of large labor intensity, low efficiency, and so on, and the fact that the existing automatic wall thickness acquisition algorithm is not accurate and adaptable, the automatic wall thickness acquisition algorithm based on the improved secondary 1.5-D spectrum estimation was proposed. The pipeline ultrasonic A-wave signals were estimated by 1.5-D spectrum that were intercepted and filled zero by the upper and lower limit of the wall thickness. Then, the 1.5-D spectrum estimation was done again, and the wall thickness information was obtained. Experimental results show that the wall thickness data generated by this algorithm is more accurate and the relative error is within 3%.
［1］李晨光. 管道焊缝无损检测的综合方法结合及图像处理［D］. 北京: 中国石油大学, 2011.［2］王忠巍. 自主海底管道机器人智能控制关键技术研究［D］. 上海: 上海交通大学, 2010.［3］戴波, 盛沙, 唐建, 等. 基于Burg最大熵法的管道腐蚀超声检测［J］. 数据采集与处理, 2008, 23(1)：112116.DAI Bo, SHENG Sha, TANG Jian, et al. Ultrasonic inspection of pipeline corrosion based on Burg maximum entropy method ［J］. Journal of Data Acquisition & Processing, 2008, 23(1)：112116.［4］Inoue H, Kishimoto K, Shibuya T. Experimental wavelet analysis of flexural waves in beams ［J］. Experimental Mechanics, 1996, 36(3)：212217.［5］林红. 小波变换在管道缺陷超声检测中的应用［J］. 装备制造技术，2009(8)：130133.LIN Hong. Wavelet transform in ultrasonic testing of pipeline application ［J］. Equipment Manufacturing Technology, 2009(8)：130133.［6］Bettayeb F, Rachedi T, Benbartaoui H. An improved automated ultrasonic NDE system by wavelet and neuron networks ［J］. Ultrasonics, 2004,42(19)：853858.［7］Acciani G, Brunetti G, Fornarelli G. Angular and axial evaluation of superficial defects on nonaccessible pipes by wavelet transform and neural networkbased classification ［J］. Ultrasonics, 2010, 50(1)：1325.［8］戴波，徐云，田小平，等. 基于SVM 多分类器的管道内检测信号处理研究［J］. 杭州电子科技大学学报, 2010, 30(4)：6571.DAI Bo, XU Yun, TIAN Xiaoping, et al. Signal processing of pipeline ultrasonic inner detection based on SVM multiclassifier ［J］. Journal of Hangzhou Dianzi University, 2010, 30(4)：6571.［9］Christopher J C. Burges. A tutorial on support vector machines for pattern recognition ［J］. Data Mining and Knowledge Discovery, 1998, 2(2)：121167.［10］陈仲生. 基于Matlab7.0的统计信息处理［M］. 长沙: 湖南科学技术出版社, 2005：185227.
