Automated processing of railway track deflection signals obtained from velocity and acceleration measurements. (September 2018)

• Record Type: Journal Article
• Authors: Milne, David; Pen, Louis L; Thompson, David; Powrie, William
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 8(2018)
• Page Start: 2097
• Page End: 2110
• Publication Date: 2018-09
• Subjects: Accelerometers -- geophones -- lineside monitoring -- performance monitoring -- railway track -- track displacement -- track vibration; Railroads -- Periodicals; Personal rapid transit -- Periodicals; 625.1
• DOI: 10.1177/0954409718762172
• Languages: English
• ISSNs: 0954-4097

Abstract

Measurements of low-frequency vibration are increasingly being used to assess the condition and performance of railway tracks. Displacements used to characterise the track movement under train loads are commonly obtained from velocity or acceleration signals. Artefacts from signal processing, which lead to a shift in the datum associated with the at-rest position, as well as variability between successive wheels, mean that interpreting measurements is non-trivial. As a result, deflections are often interpreted by inspection rather than following an algorithmic or statistical process. This can limit the amount of data that can be usefully analysed in practice, militating against widespread or long-term use of track vibration measurements for condition or performance monitoring purposes. This paper shows how the cumulative distribution function of the track deflection can be used to identify the at-rest position and to interpret the typical range of track movement from displacement data. This process can be used to correct the shift in the at-rest position in velocity or acceleration data, to determine the proportion of upward and downward movement and to align data from multiple transducers to a common datum for visualising deflection as a function of distance along the track. The technique provides a means of characterising track displacement automatically, which can be used as a measure of system performance. This enables large volumes of track vibration data to be used for condition monitoring.