A Generic Anomaly Detection of Catenary Support Components Based on Generative Adversarial Networks

Abstract

The goal of this article is to develop a universal anomaly detection approach for catenary support components (CSCs) based on the generative adversarial networks (GANs). As the long-term operation of railway system, a wide range of failures, which threaten the safe operation of vehicles, perhaps happen to CSCs. Until now, it is hard to design a generic detection system to recognize all these kinds of failures because each defect needs a special detecting algorithm for different fault signatures. The lack of anomaly samples also makes it difficult for supervised learning methods to detect effectively. In this article, a novel approach, which combines deep convolution neural networks (DCNNs) with GANs, is proposed to estimate whether failures happen and give an alarm to stop the accident. First, an object location model is trained by DCNNs to obtain numerous samples of CSCs. Second, a generative model based on deep convolutional GAN (DCGAN) is constructed to find a good mapping from image space to high-dimensional feature spaces implicitly. Finally, an anomaly rating criterion is used to diagnose images. Two typical components of CSCs, the insulator that has big body and the isoelectric line that has tiny characters, are tested here. Experiments show that the proposed method can correctly judge anomalous images of CSCs and possess a good generic failure detection ability in this single framework.