Evidence map›Paper›PMID 40431856›Full record

ArticleSensors (Basel, Switzerland)2025

A Novel Rail Damage Fault Detection Method for High-Speed Railway.

Yu Wang, Bingrong Miao, Ying Zhang, Zhong Huang, Songyuan Xu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yu WangNational Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610032, China.ORCID 0009-0004-7407-2434
Bingrong MiaoNational Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610032, China.ORCID 0000-0001-9909-3873
Ying ZhangNational Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610032, China.
Zhong HuangNational Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610032, China.
Songyuan XuNational Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610032, China.

Funding

National Natural Science Foundation of China 51775456the Basic Scientific ResearchBusiness Expenses of Central Universities - Special Research Project 2682022ZTPY007the KeyR&D projects in Sichuan Province 2023YFG0197the Self-developed Research Project of theState Key Laboratory of Traction Power 2023TPL_T08
6 · The paper itself

Abstract

With the vigorous development of speedy railway technology, steel rails, as an important structural infrastructure in speedy railways, play a crucial role in ensuring the safety of the entire speedy railway operation. A brand-new type of speedy rail inspection robot and its fault detection method are proposed to solve a number of problems, such as the difficulty and low accuracy of real-time online detection of rail defects and damage in speedy railways. The brand-new rail inspection robot is driven by two drive wheels and adopts a standard rail gauge of 1435 mm, which ensures its speedy and smooth operation on the track as well as accurate motion posture information. Firstly, 12 common types of surface damage of the rail head were analyzed and classified into five categories based on their damage characteristics. The motion state of the rail inspection robot under the five types of surface damage of the rail head was analyzed and subjected to kinematic analysis. This study analyzed the relationship between the distinctive types of damage and the motion posture of the robot during the inspection of the five types of damage. Finally, experimental tests were conducted, and it was found that the robot's motion posture would undergo sudden changes when inspecting distinctive types of injuries; the highest error rate was 3%. The effectiveness of this method was verified through experiments, and the proposed new track detection robot can greatly improve the track detection efficiency of high-speed railways and has specific academic research value and practical application value.

Indexed as

fault detectionhigh speed railwayrobotssteel rail

Identifiers

PMID40431856
PMCPMC12115127

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.