During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a signal filtering method, but its filtering performance is influenced by two parameters: the amplitude of added noise and the number of ensemble trials. To solve this issue and improve detection accuracy and distinguishability, a detection method based on improved CEEMDAN (ICEEDMAN) and the Teager energy operator (TEO) is proposed. The magnetic detection signal was first decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN with initial parameters. Signal IMFs were then distinguished using the Hurst exponent to reconstruct the preliminary filtered signal, and its maximum value (except the zero point) of the normalized autocorrelation function was defined as salp swarm algorithm (SSA) fitness. The optimal parameters that maximize fitness were found by SSA iterations, and their corresponding filtered signal was obtained. Finally, the gradient calculation and TEO were carried out to complete non-contact geomagnetic detection. The results of the simulated signal based on magnetic dipole under a noisy environment and field testing prove that ICEEMDAN denoising has better filtering performance than conventional CEEMDAN denoising methods, and ICEEMDAN-TEO has obvious advantages compared to other detection methods in the aspects of location error, peak side-lobe ratio, and integrated side-lobe ratio.

Vital defect information present in the magnetic field data of oil and gas pipelines can be perceived by developing such non-parametric algorithms that can extract modal features and performs structural assessment directly from the recorded signal data. This paper discusses such output-only modal identification method Complexity Pursuit (CP) based on blind signal separation. An application to the pipeline flaw detection is presented and it is shown that the complexity pursuit algorithm blindly estimates the modal parameters from the measured magnetic field signals. Numerical simulations for multi-degree of freedom systems show that the method can precisely identify the structural parameters. Experiments are performed first in a controlled laboratory environment secondly in real world, on pipeline magnetic field data, recorded using high precision magnetic field sensors. The measured structural responses are given as input to the blind source separation model where the complexity pursuit algorithm blindly extracted the least complex signals from the observed mixtures that were guaranteed to be source signals. The output power spectral densities calculated from the estimated modal responses exhibit rich physical interpretation of the pipeline structures.

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.

The study aimed to investigate the use of Magnetic Anomaly Detection (MAD) to locate defects in underground ferromagnetic pipelines without exposing them, which presented a large advantage over current methods. MAD has received much attention in recent years because it has many possible applications in detecting and locating ferromagnetic targets, especially for ferromagnetic pipelines. However, the accuracy of MAD is limited due to disturbs from the geomagnetic field and other sources around. The current investigation involved Variational Mode Decomposition and Scale-Space Segmentation (VMD-SSS) to improve the performance of MAD for pipeline defects. Variational Mode Decomposition (VMD) is the algorithm decomposes signals into different modes, which is a revised version of the Empirical Mode Decomposition (EMD) for analyzing non-linear and non-stationary signals. Since the geomagnetic signal is also a non-linear and non-stationary signal, and pipeline defects will affect the geomagnetic signal, it is crucial to study the bandwidth estimation of instantaneous frequency (IF) and instantaneous amplitude (IA) spectrum based on the mode. In the present work, VMD was employed to reconstruct the magnetic anomaly signals to create an effective detector, which can be used to decompose the magnetic anomaly signal into different modes accurately. The input parameters of VMD were optimized using Scale Space Segmentation (SSS), and the anti-noise performance was compared. It was noticed that VMD-SSS served as an effective tool in both extracting the time-frequency characteristics of magnetic anomaly signals and the coupling analysis of magnetic field signals between different frequency bands. The results of applying VMD-SSS in experiments showed that the peak of the coupling strength appeared when the frequency band was less than 1 Hz, and pipeline defects could be located by using the specified frequency band signal with an error of no more than 5%. The VMD-SSS method presented in the paper can be used to describe the energy coupling characteristics of the magnetic anomaly signals at different time-frequency scales and provides support for studying the mechanism and characteristics of MAD in ferromagnetic pipeline defect.