A Reconstruction Method for Weak Magnetic Pipeline Inspection Signals Based on Adaptive Multiscale Signal Reconstruction

Abstract

The weak magnetic pipeline stress detection technology is at the forefront of international pipeline safety maintenance. However, the noise embedded in weak magnetic stress detection signals significantly impacts detection accuracy. To address this issue, this article proposes a reconstruction method for weak magnetic inspection data that integrate adaptive parameter optimization and multiscale signal decomposition (MSD), aiming to enhance the detection accuracy of weak magnetic stress inspection systems. First, the method combines intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) with MSD technology to handle residual complex noise. Second, fuzzy entropy (FE) is employed for the selection of intrinsic mode functions (IMFs) to tackle the nonstationarity of weak magnetic signals. Finally, an improved sparrow search algorithm (ISSA) is introduced to dynamically adjust key parameter configurations, effectively resolving tuning difficulties and significantly improving signal reconstruction performance. The method’s performance is evaluated through the reconstruction of synthetic signals and weak magnetic simulation signals of external pipeline defects, as well as reconstruction experiments on weak excitation signals collected from Q235 pipelines. The results indicate that the method, while ensuring the highest signal-to-noise ratio (SNR), significantly reduces the total variation (TV) from 20887.5 to 1158.6 and reduces the peak distortion (Pd) factor to approximately 57, improving by about 27.62% compared to the second-best method. Furthermore, the inversion of pipeline stress distribution demonstrates that this method can effectively improve model accuracy and robustness.