[01] Saeedreza Ehteram, Control Expert of Wind Powerplants Engineering Department, MAPNA Electric & Control, Engineering & Manufacturing (MECO), Karaj, Iran. [02] Seyed Zeinolabedin Moussavi, Electrical and Computer Engineering Faculty, Shahid Rajee Teacher Training University, Lavizan, Tehran, Iran.

Determination of power plants pipelines defects is an important task in industry. This study provides a simple way to classify these defects by the use of neural network also FT (Fourier Transform) is applied to have an analysis based frequency domain and data reduction on MFL (Magnetic Flux Leakage) database. The network receives in input a matrix of defects that are derived from a simulator formula that will be explained in follow. The aim of this research approach is the audit ability for safe or non safe material in pipelines and provides a binary output for indicating whether defect is recognized or not. The network proposed is MLP (multilayer perceptron) with strictly local connections. The first layer performs local linear operations, while the second has a non linear functionality. Result shown that this procedure could be used as an appropriate solution for pipelines defect detections.

Fourier Transform, Non-Destructive Testing, Magnetic Flux Leakage, Multilayer Perceptron

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