The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/91f00d64-14ee-4019-817c-71a2befc875c

Phusakulkajorn, Wassamon ; Zeng, Yuanchen; Núñez, Alfredo ; Li, Zili (2024): Research data supporting chapter 'Unsupervised Representation Learning for Monitoring Rail Infrastructures with High-Frequency Moving Vibration Sensors' of Dissertation 'AI Solutions for Maintenance Decision Support in Railway Infrastructure'. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/91f00d64-14ee-4019-817c-71a2befc875c.v1

Dataset

• Civil Engineering
• Other Transport
• Engineering
• Transport

Autoencoders, Axle box acceleration, Empirical mode decomposition, High-frequency data, Laser Doppler vibrometer, Unsupervised learning

Restrictive Licence

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Wassamon Phusakulkajorn , Yuanchen Zeng , Alfredo Núñez , Zili Li

The data and codes were prepared and uploaded to 4TU.ResearchData by Wassamon Phusakulkajorn to support the results in Chapter 4 (Unsupervised Representation Learning for Monitoring Rail Infrastructures with High-Frequency Moving Vibration Sensors) of her dissertation. This chapter has been submitted for publication as Phusakulkajorn, W., Zeng, Y., Li, Z., Núñez, A., Unsupervised Representation Learning for Monitoring Rail Infrastructures with High-Frequency Moving Vibration Sensors.  In this research, we develop an unsupervised representation learning methodology to automatically capture the dynamic responses of rail infrastructures and provide insights into the underlying characteristics of their conditions. The objective is to address the challenge when high-frequency vibration signals are obtained in new environments where prior knowledge or reference information about infrastructure conditions is unavailable or very limited. The data used contain validated axle box acceleration data for rail defect detection and train-borne laser Doppler vibrometer data for rail fastener monitoring. The implementations are done in Python jupyter notebooks and MATLAB in which (.ipynb, .py) and (.mat) files are analytical solutions and (.eps) and (.jpg) are figures used in the main manuscript.

• 2024-07-22 first online, published, posted

4TU.ResearchData

.zip package, containing Matlab data (.mat), Matlab codes (.m), Matlab figure (.fig), figure (.eps), ReadMe file (.txt), Python jupyter notebooks .ipynb, .py, CSV files.

• ProRail ProRail
• IAM4RAIL - Holistic and Integrated Asset Management for Europe’s RAIL System (grant code 101101966) Europe’s Rail Flagship Project

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Engineering Structures, Section of Railway Engineering

E-mail name Reason

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