Multiway Analytics Applied to Railway Track Geometry and Ballast Conditions

Abstract

Railroad systems generate large amounts of data, which, when effectively analyzed, can significantly enhance maintenance decisions to improve safety and system performance. Tensor decomposition, as an advanced multidimensional data analysis tool, offers unique advantages over traditional two-way matrix factorizations, such as the uniqueness of the optimal solution and component identification, even with substantial data missing. This paper introduces the basic concepts of tensor decomposition and specifically demonstrates its application in analyzing railway track geometry and subsurface conditions. By applying tensor analysis to multidimensional data sets, the study identifies critical patterns in track geometry and ballast conditions. Key findings indicate that tensor-based models can effectively predict track deformations and align maintenance schedules more accurately, thus optimizing repair operations and extending the lifespan of railway infrastructure.