基于背景噪声降噪的管道阀门内漏量化检测  

Quantitative detection on internal leakage rate of pipeline valves based on background noise reduction

作　　者：张海峰[1] 谭东杰[1] 李柏松[1] 伍晓勇 李振林[3] 陈鑫[3] 任小龙[1] 

机构地区：[1]中国石油管道科技研究中心/油气管道输送安全国家工程实验室 [2]中国石油管道公司西安输油气分公司 [3]中国石油大学(北京)机械与储运工程学院

出　　处：《油气储运》2017年第2期214-219,共6页Oil & Gas Storage and Transportation

基　　金：中国石油天然气股份公司科技专项"油气管道关键设备国产化";2012E-2802

摘　　要：由于天然气管道阀门内漏声发射检测环境复杂、噪声干扰严重,极大地降低了阀门内漏流量的检测精度。为此,提出一种基于背景噪声的小波包软阈值降噪处理方法,并通过对降噪处理后的声发射信号采用基于支持向量回归(Support Vector Regression,SVR)方法进行输气管道阀门内漏流量的量化回归预测。结果表明:采用基于背景噪声的小波包软阈值降噪方法能够获取较为纯净的内漏源信号,降噪后获得内漏声发射信号信噪比为6.11。通过对小波包降噪处理后的特征参数进行输气管道阀门内漏流量回归预测,结果优于未进行降噪处理的预测结果,且软阈值降噪预测结果优于硬阈值预测结果,采用软阈值降噪的预测结果平均绝对比例误差为0.164,有效提高了阀门内漏流量量化回归预测的准确度。The detection accuracy of internal leakage rate of valves in gas pipelines is reduced for its acoustic emission detection is carried out in the complex environment with serious noise interference. In this paper, a wavelet packet soft threshold denoising method based on background noise was developed. And then, based on the denoised acoustic emission signals, the internal leakage rate of gas pipeline valves was quantitatively predicted by means of support vector regression （SVR）. It is shown that when the wavelet packet soft threshold denoising method based on background noise is adopted, the obtained internal leakage source signals have less noise and the SNR of acoustic emission signals of internal leakage after noise reduction is up to 6.11. Compared with the predicted internal leakage rate of gas pipeline valves without noise reduction, the regression prediction result which is obtained by using the characteristic parameters after wavelet packet denoising is better. The prediction result of sott threshold denoising is better than that of hard threshold denoising. The average absolute ratio error of prediction result based on soft threshold denoising is 0.164. Obviously, this method improves significantly the quantitative regression prediction accuracy of internal leakage rate of valves.

关 键 词：阀门 内漏 背景噪声 小波包软阈值降噪 量化检测 支持向量回归 

分 类 号：TE88[石油与天然气工程—油气储运工程]