Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/128369
Title: TRAIN WHEEL MONITORING BY RAIL PAD SENSOR AND IDENTIFICATION ALGORITHMS
Authors: ZHANG SHANLI
Keywords: wheel flat, rail pad sensor, predictive-corrective method, genetic algorithm, wheel-rail interaction force reconstruction, monitoring
Issue Date: 15-Jun-2016
Citation: ZHANG SHANLI (2016-06-15). TRAIN WHEEL MONITORING BY RAIL PAD SENSOR AND IDENTIFICATION ALGORITHMS. ScholarBank@NUS Repository.
Abstract: Wheel defects on trains, such as flat wheels, generate large dynamic impact force which deteriorate the ride quality and safety of railway operations. In this study, a novel way of vibration-based monitoring is proposed with emphasis on wheel-rail interaction force reconstruction and wheel flat identification. The major component of the monitoring system is a new rail pad sensor made of specialty plastic material that can measure the load on rail seat. To reconstruct the time history of wheel-rail interaction force from rail pad sensor output, a predictive-corrective (P-C) method is proposed. For identification of wheel flat severity (in terms of length and depth), genetic algorithm (GA) is used. The performance of the proposed wheel flat monitoring system is validated by both numerical simulation and a field test. With the rail pad sensors, an estimation of the ballast support stiffness can also be realized.
URI: http://scholarbank.nus.edu.sg/handle/10635/128369
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