Pipeline Damage Detection Based on Metal Magnetic Memory

Abstract

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.