Automated anomaly detection of catenary split pins using unsupervised learning

Split pins (SPs) are essential for maintaining the structural stability of catenary support devices (CSDs) in high-speed railroads. Excitation and vibration induced by pantograph-catenary interactions would cause SP deterioration, including but not limited to, loosening, breaking, or missing SPs. Current supervised SP inspection systems struggle to meet expectations regarding general anomaly detection . This paper presents an efficient SP inspection system based on unsupervised learning. First, a lightweight and fast object detector is designed and combined with an incremental training strategy to sequentially localize the CSD joints and SPs. Second, an unsupervised autoencoder equipped with a perceptual loss, termed as CSGAN (catenary-style generative adversarial network), is developed to accomplish the encoder-decoder process for SP reconstruction. Finally, an anomaly judgment index is integrated into this system for general SP anomaly indication. Extensive ablation and comparison experiments show the proposed approach surpasses existing state-of-the-art models in accuracy and inference speed.