Magnetic Flux Leakage signal processing in strip steel flaw area detection

Author

Jiangying Chen ; Weiting Chen ; Shijin Qian ; Delu Chen

Author_Institution

Software Eng. Inst., East China Normal Univ., Shanghai, China

Volume

2

fYear

2013

fDate

16-18 Dec. 2013

Firstpage

1096

Lastpage

1100

Abstract

The strip steel is widely used in industries, but there always exists some flaws during its manufacturing. The flaws are difficult to be detected and the analysis of the data obtained from Magnetic Flux Leakage (MFL) inspection of the strip steel is quite a challenge. In order to solve this problem, the MFL data is first processed with difference method, and then removed the baseline drift by wavelet transform. Wavelet-based NLMS adaptive filter as well as wavelet thresholding is further used to remove noises. As for the feature extraction section, k-step deviation method has been improved and successfully applied to characterize the default area.

Keywords

adaptive filters; difference equations; feature extraction; image segmentation; magnetic flux; production engineering computing; steel; strips; wavelet transforms; MFL data; difference method; feature extraction section; k-step deviation method; magnetic flux leakage signal processing; strip steel flaw area detection; wavelet thresholding; wavelet transform; wavelet-based NLMS adaptive filter; Adaptive filters; Magnetic flux leakage; Sensors; Steel; Strips; Wavelet transforms; Adaptive Filter; Baseline Drift Removing; K-step Deviation Method; Magnetic Flux Leakage Signal; Wavelet Transformation;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing (CISP), 2013 6th International Congress on

Conference_Location

Hangzhou

Print_ISBN

978-1-4799-2763-0

Type

conf

DOI

10.1109/CISP.2013.6745219

Filename

6745219