Defects detection for railway catenary system with encoder-decoder architecture

Abstract In this research, a robust and efficient damage detection methodology for identifying defects in railway catenary systems is presented. An encoder-decoder architecture supplemented by residual analysis is employed for this purpose. A novel signal segmentation strategy based on the structural features of catenaries is introduced, coupled with a quasi-Welch method designed to mitigate edge effects. The potential impact of GPS inaccuracies on detection precision is examined. Additionally, a comprehensive analysis of various normalization techniques and their significant effects on defect identification outcomes is conducted. Two primary types of defects are considered: hard points in the contact wire and periodic short-wavelength irregularities (PSWI) of the contact wire, with variations in train speeds and defect magnitudes. A defect detection criterion has been developed, facilitating rapid and automatic identification of catenary defects. This integrated approach enables effective detection of defects and accurate determination of their location and can overcome the limitations of previous approaches, such as the requirement for high sampling frequency. This work not only advances the methodology for catenary inspection but also contributes to enhancing the safety and reliability of railway operations. The innovation of this work lies in the integration of the reconstruction capabilities of the encoder-decoder architecture with a residual-based defect detection method. This synergy allows the respective features of each to complement the other effectively.