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Fatigue test loading methodfor wagon body basedon measured load

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摘要 Purpose–In this paper,the C80 special coal gondola car was taken as the subject,and the load test data of the car body at the center plate,side bearing and coupler measured on the dedicated line were broken down to generate the random load component spectrums of the car body under five working conditions,namely expansion,bouncing,rolling,torsion and pitching according to the typical motion attitude of the car body.Design/methodology/approach–On the basis of processing the measured load data,the random load component spectrums were equivalently converted into sinusoidal load component spectrums for bench test based on the principle of pseudo-damage equivalence of load.Relying on the fatigue and vibration test bench of the whole railway wagon,by taking each sinusoidal load component spectrum as the simulation target,the time waveform replication(TWR)iteration technology was adopted to create the drive signal of each loading actuator required for the fatigue test of car body on the bench,and the drive signal was corrected based on the equivalence principle of measured stress fatigue damage to obtain the fatigue test loads of car body under various typical working conditions.Findings–The fatigue test results on the test bench were substantially close to the measured test results on the line.According to the results,the relative error between the fatigue damage of the car body on the test bench and the measured damage on the line was within the range of16.03%–27.14%.Originality/value–The bench test results basically reproduced the fatigue damage of the key parts of the car body on the line.
出处 《Railway Sciences》 2023年第1期68-83,共16页 铁道科学(英文)
基金 supported by the Science and Technology Research and Development Foundation of the Ministry of Science and Technology(Grant No.2020YFB1200200ZL) the Scientific Research Program of the Department of Education of Liaoning Province(Grant No.2021LJKZ1298) the Science and Technology Research and Development Foundation of CRRC(Grant No.2021CHA014).
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