Pre-processing of Track Geometry Measurements: A Comparative Case Study

Degrading linear assets such as railway track deteriorate and lose their functionality over time and usage. A reliable and effective predictive maintenance strategy is necessary to rehabilitate the functionality and reliability of these assets. Data analytics are required to be performed to extract the information used for the decision-making process and for implementing an optimized maintenance strategy. Accordingly, data pre-processing and data quality improvement are essential to remove errors in measurements and develop efficient data analysis methods. Inaccurate measurement positioning is a common error in track geometry measurements which causes track geometry single defects suffer from an uncontrolled shift called positional error. To reduce the positional errors of track geometry measurements, this paper presents two alignment methods i.e., modified correlation optimized warping (MCOW) and recursive segment-wise peak alignment (RSPA). MCOW is a profile-based method that align all the measurements with the same priority, while RSPA is a featured-based method that only focuses on the alignment of peaks with high amplitudes in the geometry measurements. To evaluate and compare the performance of these methods in aligning track geometry measurements, a case study was conducted. The results show that RSPA can precisely align the single defects, while the MCOW is more efficient when considering the same importance for aligning every single data-point.