Abstract
Defect inversion is of special interest to magnetic flux leakage (MFL) inspection in industry. This study proposes an iterative neural network to reconstruct three-dimensional defect profiles from three-axial MFL signals in pipeline inspection. A radial basis function neural network is utilised as the forward model to predict the MFL signals given a defect profile, and the defect profile gets updated based on a combination of gradient descent and simulated annealing in the iterative inversion procedure. Accuracy of the proposed inversion procedure is demonstrated in estimating the profile of different defects in steel pipes. Experimental result based on three-axial simulated MFL data also shows that the proposed inversion approach is robust even in presence of reasonable noise.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
