A Novel Crack Quantification Method for Ultra-High-Definition Magnetic Flux Leakage Detection in Pipeline Inspection

Cracks that may cause pipeline cracking and leakage become the main risk of in-service pipelines after conventional metal loss defects have been detected. Therefore, it is imperative to develop ultra-high-definition magnetic flux leakage (MFL) detection for cracks. Due to the larger spatial sampling interval of conventional MFL inspections than the crack defect width, sparse sampling will result in problems such as missed detection of crack defects or insufficient signal sampling of crack defect MFL signals. To address the problem, this paper presents a detection topology in which the magnetic field signal is integrated first and then sampled, and it can effectively sample the leakage magnetic field of cracks. Furthermore, a magnetic field spatial integration (MFSI) method is proposed based on the magnetic dipole model, which develops an analytical solution for the depth quantification of crack defects. Using the multi-MFL spatial integral values under different lift offs, iterative equations are constructed, and the numerical solution for the width quantification of crack defects is provided. The finite element simulation and physical experiment are constructed. And, the results show that the proposed MFSI method can realize the quantification of crack size. When the crack defect aspect ratio is greater than 4, the quantization error of the crack depth and width does not exceed 0.7 mm and 0.1 mm, respectively. The robustness of the MFSI method for crack quantification is also discussed and verified. Moreover, based on theoretical analysis and simulation data, this paper concludes that the distribution of cracks MFL signal is width-insensitive, which would cause that classic MFL opening profile recognition methods for metal loss defects are no longer applicable to crack defects. Therefore, the proposed MFSI method is useful and meaningful for crack defect quantification in ultra-high-definition MFL detection.