Robust defect detection algorithm based classification on features extracted from MFL signals

ANALYTICAL models at recent years are developed to determine surface-breaking defects along the applied field when using the magnetic flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of defects. Permeability variations were neglected by employing a flux density close to sample saturation. Three different defect geometries were experimentally investigated and the validity of the analytical model was verified. Different Feature extractor functions are applied in this paper to yield fast decision and more accurate. Indeed more accuracy is because of decision on different features that yields by employing two kinds of feature extractors, PCA and DCT. Both feature extraction and Multilayer perceptron (MLP) methods for identifying erosion defects are described and employed in this paper. Great accuracy rate in compare between results of related approaches suggests that this Method can be used as an algorithm of MFL data interpretation technique.