Intelligent Data Fusion for Anomaly Detection in Dutch Railway Catenary Condition Monitoring

Abstract

Aiming to handle the increasing variety and volume of railway infrastructure monitoring data, this paper explores the use of intelligent data fusion methods for automatic anomaly detection of railway catenaries. Three classical data dimensionality reduction methods, namely the principal component analysis (PCA), the autoencoder neural network, and the t-distributed stochastic neighbor embedding (t-SNE) are adopted for the data fusion of catenary monitoring data. Then, anomaly detection can be achieved using new features that are automatically extracted from the original data, which requires no prior knowledge of the data or catenary conditions. A case study using data measured from the Dutch railway is presented to compare the performance of the three methods. Six types of catenary monitoring data, including pantograph-catenary contact force, pantograph-catenary friction force, contact wire thickness, contact wire height and stagger, are used in the presented case study. It is demonstrated that both PCA and autoencoder can detect anomalies from catenary monitoring data, while t-SNE shows little indication of such ability. Further, the autoencoder outperforms PCA in distinguishing anomalies in the case study, likely owing to its superiority in analysing data with nonlinearity. Overall, autoencoder is a promising technique for automating the anomaly detection of railway catenaries. The detection results can provide indicators for failure prediction and maintenance decision making.