title: Fast estimation of defect profiles from the magnetic flux leakage signal based on a multi-power affine projection algorithm.
abstract: Magnetic flux leakage (MFL) inspection is one of the most important and sensitive nondestructive testing approaches. For online MFL inspection of a long-range railway track or oil pipeline, a fast and effective defect profile estimating method based on a multi-power affine projection algorithm (MAPA) is proposed, where the depth of a sampling point is related with not only the MFL signals before it, but also the ones after it, and all of the sampling points related to one point appear as serials or multi-power. Defect profile estimation has two steps: regulating a weight vector in an MAPA filter and estimating a defect profile with the MAPA filter. Both simulation and experimental data are used to test the performance of the proposed method. The results demonstrate that the proposed method exhibits high speed while maintaining the estimated profiles clearly close to the desired ones in a noisy environment, thereby meeting the demand of accurate online inspection.
authors.affiliation: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China. hanwenhua@shiep.edu.cn.
authors.forename: Wenhua
authors.initials: W
authors.lastname: Han
authors.affiliation: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China. yuko1228@163.com.
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Shen
authors.affiliation: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China. daniel_xiaoxu@163.com.
authors.forename: Jun
authors.initials: J
authors.lastname: Xu
authors.affiliation: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. zeit@263.net.
authors.forename: Ping
authors.initials: P
authors.lastname: Wang
authors.affiliation: School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. g.y.tian@ncl.ac.uk.
authors.forename: Guiyun
authors.initials: G
authors.lastname: Tian
authors.affiliation: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China. zenkobe24@163.com.
authors.forename: Zhengyang
authors.initials: Z
authors.lastname: Wu