Litcius/Paper detail

Experimental measurement of track irregularities using a scaled track recording vehicle and Kalman filtering techniques

Sergio Muñoz, Pedro Urda, José L. Escalona

2021Mechanical Systems and Signal Processing27 citationsDOIOpen Access PDF

Abstract

The actual trend of railway industry on track surveying is heading up the development of fast automated methods for continuous monitoring of track quality. This paper presents a model-based methodology for the estimation of lateral and vertical track irregularities from different sensors mounted on a Track Recording Vehicle (TRV): an Inertial Measurement Unit (IMU), a gauge sensor and a position encoder. The proposed methodology, based on the Kalman filtering technique, allows a fast and accurate measurement of track irregularities, without the use of a total station (which usually makes the measurement process very slow). The proposed method has been validated through an experimental campaign carried out on a 90 m long 1:10, scaled track facility and a scaled TRV at the University of Seville. The use of the scaled TRV allows the measurement of the 90 metres long scaled track at an operation velocity of V = 0.7 m/s in only two minutes. The results of the estimation of track irregularities have been compared with a previous measurement of the mentioned scaled track using manual means, showing a good agreement: with errors lower than 0.7 mm in the short wavelength range D1, which is the most influential in the dynamic behaviour of the vehicle. Additionally, the obtained results have been analysed and compared in the different wavelength ranges, according to standards.

Topics & Concepts

Track (disk drive)Kalman filterInertial measurement unitHeading (navigation)Range (aeronautics)Computer scienceProcess (computing)Position (finance)System of measurementEncoderAcousticsEngineeringComputer visionArtificial intelligenceAerospace engineeringPhysicsOperating systemAstronomyEconomicsFinanceRailway Engineering and DynamicsRailway Systems and Energy EfficiencyStructural Health Monitoring Techniques