A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

Area of Science:

• Non-destructive testing
• Pipeline integrity assessment
• Magnetic flux leakage (MFL)

Background:

• Accurate defect quantification in MFL detection is critical for pipeline integrity.
• Traditional methods struggle with accuracy and efficiency in complex scenarios due to algorithm complexity and reliance on prior knowledge.

Purpose of the Study:

• To develop a novel defect quantification method for MFL signals that enhances accuracy and efficiency.
• To address the limitations of traditional defect inversion methods in complex detection environments.

Main Methods:

• Utilized a multi-sensor probe to fuse MFL signals under various lift-off values for enhanced defect information diversity.
• Employed the characteristic approximation approach (CAA) for defect-opening profile recognition.
• Developed an iterative algorithm based on a 3D magnetic dipole model and multi-sensor probe features to determine defect depth.

Main Results:

• The multi-sensor signal fusion (MSSF) method achieved accurate defect quantification with fewer than 15 iterations per depth calculation point.
• Quantification errors for rectangular metal loss, perforation, and conical defects were less than 10%.
• Finite element simulations and physical experiments validated the method's feasibility and performance.

Conclusions:

• The proposed MSSF method offers a significant improvement in defect quantification accuracy and efficiency for MFL-based pipeline inspection.
• The method meets practical inspection requirements, providing reliable data for pipeline integrity assessment.