Wavelet De-noising Method Analysis of Pipeline Magnetic Flux Leakage In-line Inspection Based on Coefficient of Variation

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI:10.1109/ICARCE55724.2022.10046475

G. Shi, Peng Hu, Jinzhong Chen, Chunyu Li, Hanquan Zhou, Yilai Ma

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引用次数: 1

Abstract

As an important national energy infrastructure, the oil and gas pipeline is known as the “lifeline project”. Magnetic flux leakage (MFL) testing is currently the most widely used inline inspection method for oil and gas steel pipelines, which can realize the identification, quantification and positioning of pipeline metal loss and other defects. MFL testing is equivalent testing. Eliminating noise in MFL signal plays a vital role in correctly extracting information from signal and realizing correct defect identification. In this paper, a wavelet de-noising method of MFL signal based on alternating coefficient is proposed. The method uses signal-to-noise ratio (SNR), root mean square error (RMSE), smoothness, for comprehensive evaluation. Combined with the time consumption, the appropriate wavelet denoising parameters are selected. The application of engineering inspection data shows that the method has a good application effect.

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基于变异系数的管道漏磁在线检测小波降噪方法分析

油气管道作为国家重要的能源基础设施，被称为“生命线工程”。漏磁检测是目前应用最广泛的油气钢管道在线检测方法，可实现管道金属损耗等缺陷的识别、量化和定位。MFL测试是等效测试。消除漏磁信号中的噪声对于正确提取信号中的信息，实现正确的缺陷识别起着至关重要的作用。本文提出了一种基于交变系数的磁流变信号小波去噪方法。方法采用信噪比(SNR)、均方根误差(RMSE)、平滑度进行综合评价。结合时间消耗，选择合适的小波去噪参数。工程检测数据的应用表明，该方法具有良好的应用效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)

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