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.
Highlights
Flow pipeline transportation is one of the fundamental modes in power plants
For this reason we applied a mathematical relation between the magnetic field applied on the surface and the defect properties. In this way an approach is to find exactly samples from a defect which is sorted in the surface by its various radial and depth and the pipeline Magnetic Flux Leakage (MFL) signal www.ccsenet.org/mas is recognized in an artificial algorithm to be used for training neural networks
Database of defects from MFL testing The database of the experimental MFL signals that is employed in this project is from Applied Magnetics group (AMG) in the department of physics from Queens in Canada
Summary
Flow pipeline transportation is one of the fundamental modes in power plants. It is necessary for pipeline’s security evaluation and maintenance to detect defects of the pipeline regularly using pipeline detector and obtain the precise information of the defect Bergamini, 2002) For this reason we applied a mathematical relation between the magnetic field applied on the surface and the defect properties. In this way an approach is to find exactly samples from a defect which is sorted in the surface by its various radial and depth and the pipeline MFL signal www.ccsenet.org/mas. Database preparation, feature extraction and classification of database is presented
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