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• W4308558687 abstract "Because the magnetic signal information of pipeline defects obtained by magnetic flux leakage detection contains interference signals, it is difficult to accurately extract the features. Therefore, a novel pipeline defect feature extraction method based on VMD-OSVD (variational modal decomposition - optimal singular value decomposition) is proposed to promote the signal to noise ratio (SNR) and reduce aliasing in the frequency domain. By using the VMD method, the sampled magnetic signal is decomposed, and the optimal variational mode is selected according to the rate of relative change (VMK) of Shannon entropy (SE) to reconstruct the signal. After that, SVD algorithm is used to filter the reconstructed signal again, in which the H-matrix is optimized with the phase-space matrix to enhance SNR and decrease the frequency domain aliasing. The results show that the method has excellent denoising ability for defect magnetic signals, and SNR is increased by 21.01%, 24.04%, 0.96%, 32.14%, and 20.91%, respectively. The improved method has the best denoising effect on transverse mechanical scratches, but a poor denoising effect on spiral welding position. In the frequency domain, the characteristics of different defects are varied, and their corresponding frequency responses are spiral weld corrosion > transverse mechanical cracking > girth weld > deep hole > normal pipe. The high-frequency band is the spiral weld corrosion with f1 = 153.37 Hz. The low-frequency band is normal with f2 = 1 Hz. In general, the VMD-OSVD method is able to improve the SNR of the signal and characterize different pipe defects. And it has a certain guiding significance to the application of pipeline inspection in the field of safety in the future." @default.
• W4308558687 created "2022-11-12" @default.
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• W4308558687 date "2023-04-01" @default.
• W4308558687 modified "2023-09-30" @default.
• W4308558687 title "Extraction of pipeline defect feature based on variational mode and optimal singular value decomposition" @default.
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• W4308558687 doi "https://doi.org/10.1016/j.petsci.2022.11.007" @default.
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