THz Wave Defect Detection Technology Based on Attention Autoencoder and Semisupervised Ladder Network

Insulation equipment plays an important role in mechanical support and electrical insulation in the power grid. When there are defects in the insulation equipment, the safe operation of the power grid will be seriously threatened. Non-destructive testing (NDT) is an important means to timely find hidden dangers. In view of the low reliability of defect recognition in the case of insufficient sample marks, based on autoencoder feature extraction and semisupervised networks, combined with a terahertz (THz) wave detection device, this article studies the nondestructive detection method of insulator internal defects. First, the spectrum signal of the THz wave is obtained by continuous wavelet transform. Then, for THz time-domain and frequency-domain data, autoencoders incorporating a soft attention mechanism and a channel-spatial attention mechanism are used to automatically extract features, and time–frequency domain cognition is spliced to form fusion features. Finally, a semisupervised ladder network classification model is constructed to train the algorithm efficiently and classify reliably when it is difficult to obtain labels of defective samples. Compared with other networks oriented to 1-D and 2-D data that are trained in the common supervised way, the method in this article has a better performance in classification accuracy and recall rate, which is helpful to improve the detection effect of internal defects of insulation equipment based on the THz wave.