This article develops and tests a self-contained railway track monitoring system that fits in existing vehicles without the need for speed and load control. Combining a train-borne laser Doppler vibrometer and axle box accelerometers enables synchronized measurements of train-track response under operational conditions. Utilizing a GPS antenna and video camera, we propose the multisignal processing method to obtain train-track vibrations with train position and speed. Then, we fuse the multiple signals to extract an impact index and a resonance index and further propose an interpretable anomaly detection strategy. We test the system on an operational line at 20-60 km/h under different working conditions and verify the detection results using information from conventional technologies. The impact index peaks near joints and welds, and the resonance index yields a good correlation with the measured track geometry. The developed solution achieves the detection, localization, and quantification of surface and support anomalies in railway tracks.

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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.@en