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Detection and Classification of Discontinuities using Discrete Wavelet Transform and MFL Testing

Damage caused by the degradation of steam generator tubes (SGTs), used in sodium-cooled reactors, leads to leakage of water or steam into the sodium coolant, which may further lead to a high-temperature reaction. This paper explains how to detect and classify the discontinuities that occur in the outer surface of SGTs using the magnetic flux leakage (MFL) method. Generally, SGTs experience stress corrosion cracking, wear, and mechanical damage, which can lead to catastrophic failures. MFL is one of the nondestructive testing methods that can detect a localized discontinuity on the outer surface of a SGT. The leakage flux detected by the magnetic sensor is used to extract information about the discontinuity. A multiphysics software package is used to obtain simulated MFL images showing discontinuities such as cracks, flat-bottom holes, and rectangular notches. Statistical features are extracted to detect the discontinuities in the images, and discrete wavelet transform (DWT) features are extracted to classify the discontinuities. Feed-forward back propagation training and testing of neural networks is used to classify the discontinuities. Using these techniques, a 100% discontinuity detection rate is achieved, and an average classification accuracy of 94.62% is obtained through the proposed algorithm.


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