MLP for Defect Detection from Power Plants Flow Pipelines Equipped by Principal Component Analysis

Abstract

Abstract This research effort proposes an intelligent control approach for Defect detection of flow pipelines in power plants by applying Multilayer Perceptron (MLP) for classification by which equipped with Principal Component Analysis (PCA). This fusion has been applied to have an intelligent defect detection algorithm of power plants flow pipelines. Among various methods of Non Destructive Testing (NDT), Magnetic Flux Leakage (MFL) technique is the most useful method due to its efficiency and low cost. For this reason models were developed to determine more accurate surface-breaking defects along the applied field when using the magnetic flux leakage 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 .Three different defects are analytically performed for this research. These are named Data type1 up to 3. In our previous works, we applied linear discriminate analysis (LDA) and observed that the results were more accurate in some cases but this algorithm is simpler and so fast rather than previous one, also mentioned method in this paper is so useful and could be simply simulate.