A data fusion approach for track monitoring from multiple in-service trains

• Published in: Mechanical systems and signal processing Vol. 95; pp. 363 - 379
• Main Authors: Lederman, George, Chen, Siheng, Garrett, James H., Kovačević, Jelena, Noh, Hae Young, Bielak, Jacobo
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.10.2017; Elsevier BV
• Subjects: Adaptive filters; Adaptive Kalman filter; Control theory; Data fusion; Data integration; Fault diagnosis; Feature extraction; Fuses; Inertial sensing; Kalman filters; Multisensor fusion; Position sensing; Railway tracks; Sensors; Signal processing; Vehicle-based inspection; Vibration monitoring; Signal processing; Data fusion; Adaptive Kalman filter; Inertial sensing; Vehicle-based inspection
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2017.03.023

•We use accelerometers in the cabins of passenger trains to monitor the tracks.•We propose a novel Kalman Filter to combine data from multiple trains.•With multiple trains, track changes can be detected sooner and more reliably. We present a data fusion approach for enabling data-driven rail-infrastructure monitoring from multiple in-service trains. A number of researchers have proposed using vibration data collected from in-service trains as a low-cost method to monitor track geometry. The majority of this work has focused on developing novel features to extract information about the tracks from data produced by individual sensors on individual trains. We extend this work by presenting a technique to combine extracted features from multiple passes over the tracks from multiple sensors aboard multiple vehicles. There are a number of challenges in combining multiple data sources, like different relative position coordinates depending on the location of the sensor within the train. Furthermore, as the number of sensors increases, the likelihood that some will malfunction also increases. We use a two-step approach that first minimizes position offset errors through data alignment, then fuses the data with a novel adaptive Kalman filter that weights data according to its estimated reliability. We show the efficacy of this approach both through simulations and on a data-set collected from two instrumented trains operating over a one-year period. Combining data from numerous in-service trains allows for more continuous and more reliable data-driven monitoring than analyzing data from any one train alone; as the number of instrumented trains increases, the proposed fusion approach could facilitate track monitoring of entire rail-networks.