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Onboard cable is important electrical equipment of an electric locomotive. In order to effectively evaluate the aging state of the cable, this article proposes a method to evaluate the aging state of the cable based on a new method of dielectric spectrum data processing and a new structure model of recognition algorithm. First, the log-min-max gray-scale (LMMGS) method is proposed, which transforms the dielectric parameters based on the frequency-domain dielectric spectrum (FDS) test into the gray matrix. The correlation among different dielectric parameters is retained, and the interference of their order of magnitude difference on feature extraction is reduced. Then, the new structure of spatial pyramid pooling (SPP) is improved by rectangular mesh dividing named Rectangular-SPP, which increases the number of divisible layers of the spatial pyramid, and it is used to optimize convolutional neural network (CNN), so as to remove the limitation of recognition algorithm on the size of input data and expand the scale of feature extraction. Finally, a set of methods for evaluating the aging state of onboard cable is proposed. The experimental results show that this method can effectively distinguish the insulation aging state of onboard cable. Meanwhile, the proposed Rectangular-SPP-CNN algorithm has a better performance than the conventional machine learning algorithms. [ABSTRACT FROM AUTHOR]

As a key component of the high-speed railway catenary, the insulator plays an important role in supporting the catenary and maintaining the insulation between the catenary and earth. Regular inspection using computer vision technology is an effective way to detect insulator defects and improve the catenary operation safety. However, achieving full automation of insulator defects detection is still a difficult task due to the defective sample scarcity and subtle defect features. To overcome the problem, this article proposes defect detection GANs (DefGANs) that consist of a denoising autoencoder, a discriminator, and a classifier to detect defects, which can generate defective samples from pitted latent representations, thereby improving the reliability of defect detection classifier. The proposed DefGAN can pit the latent representations of normal samples to generate defective samples. Our proposal mainly consists of two stages. In the first stage, we use cascaded deep segmentation networks (CDSNets) to extract the insulator from the catenary images. Then, the insulator area is sampled into small patches. In the second stage, the patches are fed into the proposed DefGAN to detect defects. Moreover, the defect score is determined by the anomaly probability of the classifier and reconstruction error of the denoising autoencoder. The effectiveness of our work is measured across the catenary data sets. [ABSTRACT FROM AUTHOR]