Pipeline Damage Detection Based on Metal Magnetic Memory
Pipeline Damage Detection Based on Metal Magnetic Memory
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DOI：
10.1109/tmag.2021.3084808
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发表时间：
2021-08
影响因子：
2.1
通讯作者：
Mingjiang Shi;Yanbing Liang;Mengfei Zhang;Zhiqiang Huang;Lin Feng;Zhengquan Zhou
中科院分区：
文献类型：
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作者：
Mingjiang Shi;Yanbing Liang;Mengfei Zhang;Zhiqiang Huang;Lin Feng;Zhengquan Zhou
金属磁记忆检测技术可以检测出管道的早期应力集中和不可见损伤。它可以在地磁场的作用下进行检测，而不需要事先对管道进行磁化，具有无损检测的优点。由于磁记忆信号相对较弱，实际检测到的信号会受到环境噪声、传感器抖动、管道表面沉积物等因素的影响。因此，需要对磁记忆信号进行去噪。本文在小波阈值去噪方法的基础上进行改进，采用平移不变小波去噪方法对采集的管道磁记忆信号进行去噪。实验结果表明，该方法得到的信噪比比未修正的小波阈值去噪提高了1.15%，且得到的信号更加稳定。针对磁记忆检测数据样本少的问题，比较分析了支持向量回归机、BP神经网络和粒子群优化多输出最小二乘支持向量回归（MLS-SVR）3种方法对管道缺陷总体尺寸的反演。实验结果表明，基于粒子群优化的MLS-SVR反演结果最好，总体均方误差达到0.27 mm。最后，构建了管道损伤检测系统，对不同尺寸缺陷管道的磁记忆信号进行检测，反演出缺陷的深度和半径。
The metal magnetic memory detection technology can detect early stress concentration and invisible damage of the pipeline. It can be detected under the action of the geomagnetic field, without the need to magnetize the pipeline in advance, which has the advantage of non-destructive testing. Since the magnetic memory signal is relatively weak, the actual detected signal will be affected by environmental noise, sensor jitter, and pipeline surface deposits. Therefore, the magnetic memory signal needs to be denoised. In this article, the translation invariant wavelet denoising method, which is improved based on the wavelet threshold denoising method, is used to denoise the collected pipeline magnetic memory signals. The experimental results show that the signal-to-noise ratio (SNR) obtained by this method is 1.15% higher than the unmodified wavelet threshold denoising, and the signal obtained is more stable. To solve the problem of fewer data samples for magnetic memory detection, three methods including support vector regression machine, back propagation (BP) neural network, and particle swarm optimization multiple output least-squares support vector regression (MLS-SVR) are compared and analyzed to inverse the overall defect size of pipeline defects. The experiments show that the MLS-SVR inversion result based on particle swarm optimization is the best, and the overall mean square error reaches 0.27 mm. In the end, a pipeline damage detection system is built to detect the magnetic memory signals of pipelines with different sizes of defects, and the defect depth and radius are inverted.