3-D imaging technology for determining defect of oil-gas pipeline in magnetic flux leakage testing

The magnetic flux leakage (MFL) testing is commonly used in the nondestructive evaluation (NDE) of oil-gas pipeline. The key element is to reconstruct the defect profile based on the measured MFL signals. A three-dimensional imaging technology for defect of pipeline based on a wavelet neural network (WNN) was presented. An image function matrix expressed the 3-D image parameters of defect of pipeline. The matrix elements corresponded to depth of defect in pipeline. The mapping between MFL signal and image function matrix was established by the WNN. The Mexican hat wavelet frame was used as a wavelet function and a stochastic gradient descent algorithm was adopted in the training procedure. In the experiment, the WNN was first trained to approximate the function matrix of defect image using the training data samples from both the simulated data sets for 3-D finite element model and the measured MFL signals. The trained WNN was then applied to inverse the given MFL signals and reconstruct the defect image. The testing results demonstrated that the proposed approach can successfully implement 3-D imaging and visual representation of defect in pipeline.