Abstract
The non-contact detection of buried ferromagnetic pipeline is a long-standing problem in the field of inspection of outside pipelines, and the extraction of magnetic anomaly signal is a prerequisite for accurate detection. Pipeline defects can cause the fluctuation of magnetic signals, which are easily submerged in wide-band background noise without external excitation sources. Previously, Variational Mode Decomposition (VMD) was used to separate modal components; however, VMD is based on narrow-band signal processing algorithm and the calculation is complex. In this article, a method of pipeline defect signal based on Variational Specific Mode Extraction (VSME) is employed to extract the signal of a specific central frequency by signal modal decomposition, i.e., the specific mode is weak magnetic anomaly signal of pipeline defects. VSME is based on the fact that a wide-band signal can be converted into a narrow-band signal by demodulation method. Furthermore, the problem of wide-band signal decomposition is expressed as an optimal demodulation problem, which can be solved by alternating direction method of multipliers. The proposed algorithm is verified by artificially synthesized signals, and its performance is better than that of VMD. The results showed that the VSME method can extract the magnetic anomaly signal of pipeline damage using experimental data, while obtaining a better accuracy.
Highlights
Pipeline transportation is one of the most economical and effective means to transport oil and gas to different regions [1]
We concentrate on the application of Variational Specific Mode Extraction (VSME) algorithm in pipeline defect signal extraction
In the Magnetic anomaly detection (MAD) problem, VSME is a better choice than Variational Mode Decomposition (VMD) because central frequency of the expected mode component can be determined
Summary
Pipeline transportation is one of the most economical and effective means to transport oil and gas to different regions [1]. The ferromagnetic pipeline is extremely vulnerable to damage due to internal and external defects, third-party damage, and manufacturing defects when it is buried underground, resulting in oil and gas leakage. The use of non-contact approaches for inspection and damage detection is of great relevance. This is indispensable for buried pipelines and other structural elements otherwise impossible to approach physically, but it allows performing Structural Health Monitoring (SHM) without altering the structural element under investigation. This is, for instance, the case with video-based SHM and computer vision [6,7]. While not directly applicable to buried pipelines, this is doable for external
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