Fast identification method of defect signal for in-line inspection of magnetic flux leakage
-
摘要: 漏磁内检测缺陷信号分析识别是漏磁检测技术的关键部分,为了快速识别管体缺陷信号,提高数据分析的准确率和效率,开发了基于低通滤波和差分的信号处理算法,并对漏磁内检测探头的各通道检测信号进行降噪优化处理,确定了管体缺陷识别规则。工程检测结果表明,处理过的检测信号清晰、易于快速识别,开挖检测结果与信号识别结果一致,该信号处理方式有助于快速准确识别管体缺陷信号。Abstract: The analysis and identification of defect signals was a key part of magnetic flux leakage inspection technology. In order to quickly identify pipe defect signals and improve the accuracy and efficiency of data analysis, a signal processing algorithm based on low-pass filtering and differential was developed. The noise reduction and optimization processing were carried out on the detection signals of each channel of the magnetic flux leakage detection probe, and pipe defect recognition rules were formulated. Engineering test results showed that the detection signals were clear and easy to identify quickly, the excavation detection results were consistent with the signal identification results, and the signal processing method helped to identify the pipe defect signal quickly and accurately.
-
-
[1] 呼婧,刘思娇,郑莉,等.基于动态磁多极子场的管道内外壁缺陷区分方法[J].油气储运,2021,40(6):673-678. [2] CHEN P C,LI R,FU K A,et al.Research and method for In-line inspection technology of girth weld in long-distance oil and gas pipeline[J].Journal of Physics:Conference Series,2021,1986(1):012052.
[3] 王书怡,富宽,王亚楠,等.基于组合滤波的漏磁内检测数据特征无损压缩方法[J].油气储运,2023,42(3):306-312. [4] 马义来,何仁洋,陈金忠,等.基于FPGA+ARM的管道漏磁检测数据采集系统设计[J].无损检测,2017,39(8):71-74. [5] 杜文飞,李春光,万四海.管道漏磁检测的智能方法综述[J].西南师范大学学报(自然科学版),2022,47(6):1-7. [6] 赵翰学,张咪,郭岩宝,等.基于机器学习的管道金属损失缺陷识别方法[J].石油机械,2020,48(12):138-145. [7] 郑莉,呼婧,许振丰,等.管道动态磁化漏磁内检测信号的影响因素[J].无损检测,2017,39(10):1-7,27. [8] 赵番,汤晓英,王继锋,等.金属管道内外壁缺陷的脉冲涡流检测系统[J].无损检测,2020,42(6):58-62. [9] 高鹏飞. 基于漏磁原理的管道缺陷检测与识别方法研究[D]. 沈阳:沈阳工业大学,2020. 欢迎网上投稿欢迎订阅欢迎刊登广告
计量
- 文章访问数: 11
- HTML全文浏览量: 0
- PDF下载量: 4
下载:
