Facilitating maintenance decisions on the Dutch railways using big data: The ABA case study

This paper discusses the applicability of Big Data techniques to facilitate maintenance decisions regarding railway tracks. Currently, in different countries, a huge amount of railway track condition-monitoring data is being collected from different sources. However, the data are not yet fully used because of the lack of suitable techniques to extract the relevant events and crucial historical information. Thus, valuable information is hidden behind a huge amount of terabytes from different sensors. In this paper, the conditions of the 5V's of Big Data (Volume, Velocity, Variety, Veracity and Value) in railway monitoring systems are discussed. Then, general methods that can be applied to facilitate the decision of efficient railway track maintenance are proposed for railway track condition monitoring. As a benchmark, axle box acceleration (ABA) measurements in the Dutch tracks are used, and generic reduction formulations to address new relevant information and handle failures are proposed.