Identification of railroad ballast fouling through statistical process control on ballast particle movement

Ballast fouling is an undesirable condition which adversely impacts track stability. As early detection of this condition is critical for the safety of train operations, various inspection technologies have been developed to provide information about state of the ballast and its substructure. Several studies have been conducted with the aim of providing a method for automatic and continuous monitoring of the track system. Ongoing efforts are focused on the development of techniques that enable real-time analysis of the ballast condition while remaining compatible with existing technologies. As an attempt toward that end, this study was designed to utilize statistical pattern recognition technique with data input from an innovative wireless sensor called RTS SmartRockTM (referred as SmartRock in this paper). This method was implemented in a field experiment on two sections of railroad track with homogenous traffic but different track conditions: one with clean ballast and the other with mud pumping. Four SmartRock sensors were installed at different locations on each section. With time-history data from the SmartRocks, an unsupervised statistical model was developed and evaluated for damage detection. The algorithm used was successful in distinguishing between data sets obtained from different ballast conditions. The results of the study show potential suitability of this method for timely detection of ballast fouling, thereby facilitating maintenance planning of rail tracks for a safer and more efficient transportation system.

Automated summary

This study utilizes statistical pattern recognition technique and data input from SmartRock sensors to develop a method for timely detection of ballast fouling. The method successfully distinguishes between different ballast conditions based on the data sets obtained. The study shows the potential suitability of this method for maintenance planning.