Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements
1
State Key Laboratory of Rail Traffic Control and Safety; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2
Beijing JRM Track Technology Service Co., Ltd., Beijing 100070, China
3
Beijing National Railway Research & Design Institute of Signal & Communication Group Co., Ltd., Beijing 100070, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 872; https://doi.org/10.3390/s20030872
Received: 7 January 2020
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Revised: 29 January 2020
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Accepted: 3 February 2020
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Published: 6 February 2020
(This article belongs to the Special Issue Sensors, Big Data Analytics and Modeling for Infrastructure Monitoring and Maintenance)
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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Keywords:
railway track; in-service vehicle acceleration measurement; bias correction; defect diagnosis; data-driven model
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MDPI and ACS Style
Bai, L.; Liu, R.; Li, Q. Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements. Sensors 2020, 20, 872. https://doi.org/10.3390/s20030872
AMA Style
Bai L, Liu R, Li Q. Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements. Sensors. 2020; 20(3):872. https://doi.org/10.3390/s20030872
Chicago/Turabian StyleBai, Lei, Rengkui Liu, and Qing Li. 2020. "Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements" Sensors 20, no. 3: 872. https://doi.org/10.3390/s20030872
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