埋地管道防护层缺陷现场检测与评价

中国腐蚀与防护学报

2004, Vol. 24

Issue (2): 100-104

研究报告

本期目录 | 过刊浏览

埋地管道防护层缺陷现场检测与评价

高志明;宋诗哲;王守琰;陈世利;赖广森

天津大学材料学院

ON THE SPOT DETECTION AND EVALUATION OF UNDERGROUND PIPELINE COATINGS DEFECTS

;;Shouyan Wang;;

天津大学材料学院

全文: PDF(224 KB)

摘要: 建立埋地管道防护层检测快速分析系统,实现防护层缺陷定位和防护层缺陷形式的判断。防护层缺陷定位应用密间隔电位（CIPS）二进小波变换方法，分别对CIPS数据通电电位、断电电位和二者之差进行分析，利用二次信息突出缺陷位置。在对防护层缺陷形式判断时，建立针对恒电流瞬态响应的小波－Kohonen神经网络，网络对恒电流瞬态响应曲线以多分辨分析方法分解后，利用第五层分解概貌信息，对管道防护层状态进行自适应识别。应用CIPS和恒电流瞬态响应方法在大港油田港－沧线进行现场检测，利用建立的分析系统分析和判别防护层状况，判断结果与开挖情况相符，结果令人满意。

关键词 ： CIPS, 埋地管道, 防护层, 神经网络

Abstract：The quick analysis system for underground pipeline coating detection has been set up. Defect orientation of the system is made by using the dyadic wavelet transform of ON, instant OFF close interval potential survey (CIPS) potential and the difference of them. It is indicated that the extreme values of detail-coefficients of dyadic wavelet transform will increase with the scale at the damaged points otherwise it will decrease with the scale at the other places. With the product of the third layer detail-coefficients and the product of the third layer smoothed-coefficients of those three curves obtained, the damaged points of the coating could be easily found. To diagnose the coating state of the system the wavelte Kohonen neural network is set up. The front layers of the network are corresponding to multiresolution decompositions, which has the ability of picking up the information. The last layer of the model is corresponding to Kohonen neural network, which has the ability of self-training. The coating state can be quickly diagnosed after the galvanostatic transient response is input to the model. The CIPS is used in pilot detection. Then the damaged points of the coating can be made certain by the defect orientation part of the system. The galvanostatic transient response method is used to detect the coating state of buried pipelines at the damaged point. Then the coating state can be judged by the coating state diagnosis part of the system. The detection result of real pipeline between Dagang and Cangzhou of Tianjin Dagang oil field was satisfactorily.

Key words： Close Interval Potential Survey pipeline coating neural network

收稿日期: 2003-12-17

ZTFLH:

TG172

通讯作者: 高志明 E-mail: gaozhiming@eyou.com

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引用本文:

高志明; 宋诗哲; 王守琰; 陈世利; 赖广森 . 埋地管道防护层缺陷现场检测与评价[J]. 中国腐蚀与防护学报, 2004, 24(2): 100-104 .
Shouyan Wang. ON THE SPOT DETECTION AND EVALUATION OF UNDERGROUND PIPELINE COATINGS DEFECTS. J Chin Soc Corr Pro, 2004, 24(2): 100-104 .

链接本文:

https://www.jcscp.org/CN/ 或 https://www.jcscp.org/CN/Y2004/V24/I2/100

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