摘要详情

ID / 提交时间

94 / 2024-08-29 10:24:06

标题

Extraction of Strong Periodic Time Frequency Characteristics and Regression Model for High-Speed Railway Track-Vehicle System

关键字

Vertical acceleration of the car body; Periodic feature extraction; Variational Mode Decomposition; Inspection differences; normalization

主题及专题

Special Sessions > SS05 Signal Decomposition-Integrated Intelligent Mechanical Fault Diagnosis and Remaining Useful Life Prediction

状态

终稿

作者

TaoKai / Infrastructure Inspection Research Institute； China Academy of Railway Sciences Corporation Limited

GuoJIanfeng / Infrastructure Inspection Research Institute； China Academy of Railway Sciences Corporation Limited

YangJinsong / Infrastructure Inspection Research Institute； China Academy of Railway Sciences Corporation Limited

ShaoQi / Infrastructure Inspection Research Institute； China Academy of Railway Sciences Corporation Limited

PengNan / Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited

LiuJinzhao / Infrastructure Inspection Research Institute； China Academy of Railway Sciences Corporation Limited

摘要

In the process of evaluating the line status using vehicle dynamic response, to address the issue of significant differences in the acceleration response of different high-speed trains under the same track irregularity excitation conditions, a lightweight regression model was constructed to normalize car body acceleration inspection data. Firstly, analyze and extract the periodic characteristics of track geometry and car body acceleration through VMD algorithm. Secondly, using energy distribution, determine the strong correlation between periodic vertical acceleration of car body and track geometry. Finally, a lightweight regression model is constructed to normalize the measured acceleration data of different types of inspection vehicles. The results show that for different comprehensive inspection vehicles, the normalized vertical acceleration of the car body is at the same level.