TY - DATA T1 - 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' PY - 2024/07/22 AU - Wassamon Phusakulkajorn AU - Yuanchen Zeng AU - Alfredo Núñez AU - Zili Li UR - DO - 10.4121/91f00d64-14ee-4019-817c-71a2befc875c.v1 KW - Unsupervised learning KW - Axle box acceleration KW - Laser Doppler vibrometer KW - Autoencoders KW - Empirical mode decomposition KW - High-frequency data N2 -

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.

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