Rail hidden damage detection based on the modal curvature differences method

The rail hidden damage includes rail bottom and rail jaw cracks, which cannot be easily detected during the early development stage, and pose a significant danger and risk of rail-breaking accidents. This paper proposed an effective method to detect rail hidden damage by establishing a rail crack extension analysis model based on modal curvature difference theory and verified by the self-developed device. The results showed the distance of the rail bottom crack from the central axis of the rail bottom and the peak value of the modal curvature difference represented an approximate one-dimensional Gaussian correlation. The crack depth and crack length showed an approximately linear correlation with the peak value of the modal curvature difference. Setting cracks at rail bottom and rail jaw, the detection errors were below 5% at a 6 mm crack depth and lower than 3% at a 10 mm crack depth. The results of signal-to-noise ratio during rail bottom crack detection indicated that crack could be effectively detected before rapid expansion when the crack distance from the rail bottom central axis exceeded 35 mm, and crack parameters could be quantitatively determined before rapid expansion at a crack distance exceeding 45 mm.

Authors

Institutions

Publication Details

Journal
Advances in Mechanical Engineering
Published
2024-12-01
DOI
https://doi.org/10.1177/16878132241279500
Citations
2
Primary Topic
Ultrasonics and Acoustic Wave Propagation
Type
article
Field-Weighted Citation Impact
0.54

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rail hidden damage detection based on the modal curvature differences method

Guojian Zhou, Weilan Li, Qiutong Li, Xianpu Yuan et al.
2 citations
Advances in Mechanical Engineering
Ultrasonics and Acoustic Wave Propagation
0.54
article

Rail hidden damage detection based on the modal curvature differences method

Guojian Zhou, Weilan Li, Qiutong Li, Xianpu Yuan, Yan Liu
article en
2 citations

Abstract

The rail hidden damage includes rail bottom and rail jaw cracks, which cannot be easily detected during the early development stage, and pose a significant danger and risk of rail-breaking accidents. This paper proposed an effective method to detect rail hidden damage by establishing a rail crack extension analysis model based on modal curvature difference theory and verified by the self-developed device. The results showed the distance of the rail bottom crack from the central axis of the rail bottom and the peak value of the modal curvature difference represented an approximate one-dimensional Gaussian correlation. The crack depth and crack length showed an approximately linear correlation with the peak value of the modal curvature difference. Setting cracks at rail bottom and rail jaw, the detection errors were below 5% at a 6 mm crack depth and lower than 3% at a 10 mm crack depth. The results of signal-to-noise ratio during rail bottom crack detection indicated that crack could be effectively detected before rapid expansion when the crack distance from the rail bottom central axis exceeded 35 mm, and crack parameters could be quantitatively determined before rapid expansion at a crack distance exceeding 45 mm.

Advances in Mechanical EngineeringVol. 16(12)
Shanghai Research Institute of Materials (CN)
National Key Research and Development Program of China
Sustainable cities and communities
Openalex Percentile: Top 38%
Ultrasonics and Acoustic Wave Propagation
0.54
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Rail hidden damage detection based on the modal curvature differences method — Guojian Zhou, Weilan Li, et al. · Advances in Mechanical Engineering (2024) | TGRS Research Map | TGRS