Defects detection for railway catenary system with encoder-decoder architecture
This study presents a robust and efficient damage detection methodology for railway catenary systems, employing an encoder-decoder architecture supplemented by residual analysis. A novel signal segmentation strategy based on catenary structural features is introduced, coupled with a quasi-Welch method to mitigate edge effects. The investigation examines the impact of GPS inaccuracies on detection precision and conducts a comprehensive analysis of normalization techniques and their effects on defect identification. Two primary defect types are considered: hard points in the contact wire and periodic short-wavelength irregularities (PSWI), with variations in train speeds and defect magnitudes. A defect detection criterion is developed, enabling rapid and automatic identification of catenary defects. The integrated approach facilitates effective detection and accurate localization, overcoming limitations of previous methods such as the requirement for high sampling frequency. This work advances catenary inspection methodology and contributes to enhancing railway safety and reliability. The innovation lies in integrating the reconstruction capabilities of the encoder-decoder architecture with a residual-based defect detection method, allowing complementary features to synergistically improve detection performance.