Defects Classification of Steel Tube Based on Spectrogram and CNN Using Magnetic Flux Leakage Signals

Magnetic flux leakage (MFL) nondestructive testing technology uses magnetic sensors to detect the magnetic signal on the surface of seamless steel tube. In this process, the magnetic sensors are used to detect the flux leakage signal from the defects exist in the tube. The defects are located on the external or internal surface of the tubes with significant longitudinally or transversely oriented forms. This paper proposes a defect recognition and classification method based on spectrogram and convolutional neural network (CNN), which converts magnetic flux leakage signals frames obtained by sensors into spectrograms and sends them into convolutional neural network as input data for defect classification. The experiments show that the steel tube defects classification performance was 71% for method proposed in this paper.