Novel image-free arc detection method for pantograph–catenary systems based on direct DWT–ANN signal analysis

Arc faults in pantograph catenary systems pose a significant threat to the reliability, safety, and efficiency of the electric railways. However, Conventional approaches relying on image processing and deep learning are hindered in real-time applications due to computational delays exceeding the arc time constant. Therefore, this study proposes a novel image-free arc detection method that directly analyzes measured traction current signals using DWT-ANN (Discrete Wavelet Transform-Artificial Neural Networks). The significance of this work lies in its ability to extract transient arc features directly from the traction current waveform, providing a computationally efficient solution for real-time monitoring without the need for additional sensing modalities. Various mother wavelet families are evaluated, and specific detail levels are identified as the most informative for capturing arc transients. Among these, Daubechies db9 and Symlet sym8 showed the highest discriminative performance and were used as inputs to a compact ANN structure. The network, trained with normalized feature data, achieved a high regression coefficient, indicating excellent classification accuracy. Additionally, algorithm robustness was validated by training the neural network on db4-extracted features and testing it across all accepted wavelets, with consistent detection performance. The results confirm the robustness and practicality of the proposed DWT-ANN framework for real-time arc detection in railway systems.