Abstract
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.
Highlights
Track health depends on the track geometry condition and the condition of each track component [1]
We propose a data-driven bias correction and defect diagnosis model for in-service vehicle acceleration measurements (DBCDD-IVAM)
In view of the above, we proposed a data-driven bias correction and defect diagnosis model for in-service vehicle acceleration measurements (DBCDD-IVAM) to eliminate false positive detections of track vibration defects and to diagnose the causes of track vibration defects
Summary
Track health depends on the track geometry condition and the condition of each track component (such as rails, sleepers, fasteners, and ballast) [1]. Lee et al [22] proposed a mixed filtering approach for finding track vertical and lateral irregularities, using axle box and bogie acceleration signals from in-service high-speed trains. The proposed model was developed by exploring the relationship between substantial amounts of historical vibration data for the cabin of a train, as measured by track quality instruments and multiple track condition measurements from other inspection methods. The proposed method eliminates false positive detections of track vibration defects by track quality instruments and diagnoses the causes of track vibration defects (i.e., severe vertical and lateral vibrations of the cabin).
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