Abstract
One of the most challenging scientific industrial courses in recent years is intelligent defect detection. 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. By hiring a BELBIC (Brain Emotional Learning Based Intelligent Controller) controller on the extracted features, the results are more accurate in some cases. Linear Discriminate Analysis (LDA) is another helpful instrument that is employed precise decision. All feature extractions LDAs and Multilayer perceptron (MLP), 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.
Highlights
The pipeline transportation is one of the fundamental modes in petroleum and natural pipeline transportation
An approach is to classifying and performs a true decision. These are some different kinds of neural networks such as Multilayer Perceptron (MLP), Learning Vector Quantization (LVQ) (Martin Golz, David Sommer, 2006), Self Organized Machine (SOM) (Hiroshi Wakuya, Hiroyuki Harada, Katsunori Shida, 2007) and so on
In order to investigate the statistical distribution of the error rate, three neural networks with the same structure and transfer functions were trained with the not same data set (Saeedreza Ehteram, Ali Sadr, Seyed zeinolabedin Mousavi, 2007)( R
Summary
The pipeline transportation is one of the fundamental modes in petroleum and natural pipeline transportation. Finding the intellective technology to recognize pipeline defect quantitatively is urgent This is a time consuming and tiring task; the result depends on human elements of uncertainty. In this work, an approach for the automatic detection of a defect is presented, where the NDE data are preprocessed using an analytical model of the magnetic flux and the extracted information is passed on to a panel of neural networks. This peper mentions employment of LDA in the application of defect detection
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