Estimation of Railway Track Longitudinal Profile Using Vehicle-Based Inertial Measurements

Quirke, Paraic; O’Brien, Eugene J.; Bowe, Cathal; Cantero, Daniel

doi:10.1007/978-3-319-75390-4_12

Cited by 1 publication

(2 citation statements)

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“…However, little research has been conducted on how to address the false positive detections of track vibration defects by such instruments. Many researchers have proposed models for detecting track defects using in-service vehicle acceleration measurements [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Most of the models or algorithms proposed are based solely on vehicle acceleration measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Lederman et al [8] proposed a data-driven approach that extracts features to identify two types of track changes (specifically, track replacement and tamping) by using vertical and lateral vibration data from accelerometers in the cabin of an in-service train. OBrien et al [9][10][11][12] proposed a cross-entropy combinatorial optimization method to detect track vertical irregularity using vertical accelerations and angular velocities measured using sensors mounted on a train bogie. Quirke et al [13] proposed a cross-entropy combinatorial optimization method that identifies railway bridge damage using bogie vertical acceleration signals from an operational train.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements

Bai

Liu

2020

Sensors

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Track quality instruments use low-cost accelerometers placed on or attached to the floors of operating trains, and these instruments collect substantial amounts of data over short inspection periods. The measurements collected by the instruments are the main data source for track irregularity evaluation. However, considerable measurement bias exists in the vertical and lateral vibration data obtained from such instruments. False positive track vibration defects detected by track quality instruments occur frequently. This results in considerable time and effort being expended needlessly because maintenance workers have to visit the railway track sites to check and review the track vibration defects. Therefore, we propose a model for data-driven bias correction and defect diagnosis for in-service vehicle acceleration measurements based on track degradation characteristics. Substantial amounts of historical track measurement data from different inspection methods were mined extensively to eliminate the false positive detection of track vibration defects and diagnose the causes of track vibration defects. Actual measurement data from the Lanxin Railway were used to validate our proposed model. The success rate achieved in identifying false positive track vibration defects was 84.1%, and that in track vibration defect diagnosis was 75.8%. These high success rates suggest that the proposed model can be of practical use in improving railway track maintenance management.

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