Unsupervised anomaly detection in railway catenary condition monitoring using autoencoders

Abstract

The condition monitoring of railway infrastructures is collecting big data for intelligent asset management. Making the most of the big data is a critical challenge facing the railway industry. This study focuses on one of the main railway infrastructures, namely the catenary (overhead line) system that transmits power to trains. To facilitate the effective usage of catenary condition monitoring data, this study proposes an unsupervised anomaly detection approach as a pre-processing measure. The approach trains autoencoders to reduce the dimensionality of multisensor data and generate discriminative features between healthy and anomalous data. By testing the reconstruction errors using the trained autoencoders, anomalous data that indicate potential defects of catenary can be identified without prior information and human intervention. A case study on a section of high-speed railway catenary in China shows that the approach can automatically distinguish between healthy and anomalous data. The output anomalous data can save a considerable amount of computation time and manpower in further interpretations aiming to pinpoint defects.