Utilizing a Pattern Recognition Controller and Linear Discriminate Analysis for MFL Defect Detection

Intelligent defect detection attracts lots of attentions and efforts in the last decade due to high amount of money which is wasted every year because of undetectable defects. Among various methods, Non Destructive Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. Models were 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. In our previous works, we applied BELBIC (Brain Emotional Learning Based Intelligent Controller) controller on the extracted features and observed that the results were more accurate in some cases. Linear Discriminate Analysis (LDA) is another helpful instrument that is employed precise decision. But for this paper, we decided to apply LDA and BELBIC serially and observe the results. This method was so useful and more precise results were provided. All feature extractions LDAs, Multilayer perceptron (MLP,) and BELBIC are 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 .