Identification Methods for Modal Parameters of Track Structures and Their Application Status and Prospects

Author:

An Bolun1ORCID,Wang Pu1,Liu Fengshou1,Yang Guang1,Ma Chaozhi2ORCID,Ma Junqi3

Affiliation:

1. China Academy of Railway Sciences Corporation Limited, Beijing, P. R. China

2. School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, P. R. China

3. Graduate Department, China Academy of Railway Sciences, Beijing, P. R. China

Abstract

Rail transit’s wheel–rail system periodically encounters defects such as wheel polygons, rail corrugation, and rail fastener failure, which are intricately linked to the modal parameters of track structures. Identifying these modal parameters is essential for refining wheel–rail dynamics models, understanding track defect mechanisms, and defect detection. This study reviews the current methodologies for identifying track structure modal parameters, emphasizing their significance in track engineering. It categorizes various identification techniques, examines their development, and highlights their application in updating track dynamics theoretical models. The relationship between track modal parameters and wheel–rail defects is discussed, alongside a summary of modal parameter-based defect remediation strategies globally. The paper also evaluates the current state of defect identification research utilizing track modal parameters. In the “prospects” section, three forward-looking research avenues are proposed. These approaches are poised to streamline and improve the efficiency of modal parameter extraction, marking potential breakthroughs in the field.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Technology Research and Development Plan of China Railway

Fund of China Academy of Railway Sciences Corporation Limited

Publisher

World Scientific Pub Co Pte Ltd

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3