Evidence map›Paper›PMID 39771794›Full record

ArticleSensors (Basel, Switzerland)2024

Identification Method for Railway Rail Corrugation Utilizing CEEMDAN-PE-SPWVD.

Jianhua Liu, Kexin Zhang, Zhongmei Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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

3 authors.

Jianhua LiuCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.ORCID 0000-0002-1694-0975
Kexin ZhangCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.
Zhongmei WangCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.

Funding

the National Key R&D Program of China 2021YFF0501101The National Natural Science Foundation of China 52272347the National Science Fund of Hunan 2024JJ7132the Scientific Research Project of Hunan Provincial Department of Education 22A0391
6 · The paper itself

Abstract

Rail corrugation intensifies wheel-rail vibrations, often leading to damage in vehicle-track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), permutation entropy (PE), and Smoothed Pseudo Wigner-Ville Distribution (SPWVD). Initially, vertical acceleration data from the axle box are decomposed using CEEMDAN to extract intrinsic mode functions (IMFs) with distinct frequencies. PE is used to evaluate the randomness of each IMF component, discarding those with high permutation entropy values. Subsequently, correlation analysis is performed on the retained IMFs to identify the component most strongly correlated with the original signal. The selected component is subjected to SPWVD time-frequency analysis to identify the location and wavelength of the corrugation occurrence. Filtering is applied to the IMF based on the frequency concentration observed in the time-frequency analysis results. Then, frequency-domain integration is performed to estimate the rail's corrugation depth. Finally, the algorithm is validated and analyzed using both simulated data and measured data. Validation results show that this approach reliably identifies the wavelength and depth characteristics of rail corrugation. Additionally, the time-frequency analysis results reveal variations in the severity of corrugation damage at different locations.

Indexed as

complete ensemble empirical mode decompositionrail corrugation identificationsmoothed pseudo Wigner–Ville distributiontime–frequency characteristic analysis

Identifiers

PMID39771794
PMCPMC11680013

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Registered trials

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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.