Early wheel flat detection: an automatic data-driven wavelet-based approach for railways-Reference-Cited by-同舟云学术

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Early wheel flat detection: an automatic data-driven wavelet-based approach for railways

Published:2022-07-28 Issue:6 Volume:61 Page:1644-1673
ISSN:0042-3114
Container-title:Vehicle System Dynamics
language:en
Short-container-title:Vehicle System Dynamics

Author:

Mosleh Araliya¹^ORCID,Meixedo Andreia¹^ORCID,Ribeiro Diogo²^ORCID,Montenegro Pedro¹^ORCID,Calçada Rui¹^ORCID

Affiliation:

1. CONSTRUCT – LESE, Faculty of Engineering, University of Porto, Porto, Portugal

2. CONSTRUCT – LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal

Funder

CONSTRUCT – Instituto de Estruturas e Construções

FCT/MCTES

European Regional Development Fund

Operational Programme for Competitiveness and Internationalisation

Lisbon Regional Operational Programme

PORTUGAL 2020 Partnership Agreement

Stimulus of Scientific Employment, Individual Support

FCT – Fundação para a Ciência e Tecnologia

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Safety, Risk, Reliability and Quality,Automotive Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00423114.2022.2103436

Reference65 articles.

1. Out-of-round railway wheels-a literature survey

2. A Review of the Effects of Out-Of-Round Wheels on Track and Vehicle Components

3. Out-of-round railway wheels—a study of wheel polygonalization through simulation of three-dimensional wheel–rail interaction and wear

4. Wear damage of out-of-round wheels in rail wagons under braking

5. Impact Loads due to Wheel Flats and Shells

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3. An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine;Engineering Applications of Artificial Intelligence;2024-07

4. Enhancing nonlinear dynamics analysis of railway vehicles with artificial intelligence: a state-of-the-art review;Nonlinear Dynamics;2024-06-22

5. A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders;Railway Engineering Science;2024-06-19

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