Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder-Reference-Cited by-同舟云学术

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Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder

Published:2024-03-27 Issue: Volume: Page:1-26
ISSN:0042-3114
Container-title:Vehicle System Dynamics
language:en
Short-container-title:Vehicle System Dynamics

Author:

Jorge T.¹,Magalhães J.¹,Silva R.²,Guedes A.¹,Ribeiro D.¹^ORCID,Vale C.²^ORCID,Meixedo A.²,Mosleh A.²^ORCID,Montenegro P.²^ORCID,Cury A.³

Affiliation:

1. CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal

2. CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Porto, Portugal

3. Graduate Program in Civil Engineering, Federal University of Juiz de Fora, Juiz de Fora, Brazil

Funder

European Regional Development Fund

Portuguese National Funds

Foundation for Science and Technology

MCTES

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00423114.2024.2333771

Reference74 articles.

1. Wheel Defect Detection Using a Hybrid Deep Learning Approach

2. Lagnebäck R. Evaluation of wayside condition monitoring technologies for condition-based maintenance of railway vehicles. 2007.

3. State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

4. A dynamic wheel–rail impact analysis of railway track under wheel flat by finite element analysis

5. Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays

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