Abstract

This paper presents a methodology for classifying train passages into different types with a weigh-in-motion (WIM) system to allow the calibration of railway fatigue load models and identify individual vehicles from the measurements for the continuous calibration of railway WIM stations from in-service trains. The quality assurance of the measured responses is demonstrated using statistical methods. This paper discusses the measurement station, the method used for processing the raw data, the algorithm used to identify the train types and vehicles automatically, and the limits of the obtained load spectra. The measurement errors are demonstrated to be satisfying for use in fatigue load model calibration. Furthermore, this paper proposes actions for accurately obtaining the actual traffic conditions and describes the future work required in this area.

Highlights

  • Railway wheel loads’ monitoring data are of key importance in monitoring the dynamic behavior of a vehicle and railway track, and in the online tracking of actual train loads and possible imbalances

  • This paper presents an algorithm for the automatic identification of train types as well as locomotives and wagons based on a priori information to fill this gap

  • This paper presents an approach that can identify train types for fatigue load model calibration and vehicles for the continuous calibration of the railway WIM station

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Summary

Introduction

Railway wheel loads’ monitoring data are of key importance in monitoring the dynamic behavior of a vehicle and railway track, and in the online tracking of actual train loads and possible imbalances. They are used in the study of traffic safety and in the maintenance planning of the track and bridges, which are very important parts of the railway system. The B-WIM system [2] measures deformation on the level of bridge and calculates axle weights using Moses algorithm [7] The basic of this algorithm is to minimize the difference between the measured and predicted bridge response based on the Influence Lines (IL)

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