Machine learning in railway catenary inspection: A review of data processing techniques

In electrified railways, the dynamic behavior of the catenary system is crucial for ensuring smooth and reliable current collection by electric trains. This process depends on the direct interaction between the overhead catenary wires and the pantograph mounted on the train roof. While manual inspection remains the standard practice, automated catenary assessment techniques offer significant advantages in terms of efficiency, accuracy, and safety. This paper reviews the state-of-the-art research on the data processing related to railway catenary inspection and discusses the challenges and prospects of machine learning technology. The insights will not only help advance the integration of machine learning methods into railway catenary inspection but also enable the design of smarter inspection equipment and improve the efficiency and accuracy of catenary inspection strategies.