Magnetic Flux Leakage signal processing in strip steel flaw area detection

摘要

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.