Research data supporting chapter 'SNN with Time-Varying Weights for Rail Squat Detection' of Dissertation 'AI Solutions for Maintenance Decision Support in Railway Infrastructure'

The data and codes were prepared and uploaded to 4TU.ResearchData by Wassamon Phusakulkajorn to support the results in Chapter 3 (SNN with Time-Varying Weights for Rail Squat Detection) of her dissertation. This chapter has been submitted for publication as Phusakulkajorn, W., Hendriks, J.M., Li, Z., Núñez, A., Spiking Neural Network with Time-Varying Weights for Rail Squat Detection. In this research, we develop a spiking neural network (SNN) with time-varying weights to detect rail surface defects, e.g., squats, of varying severity levels, using ABA measurements. This method aims to improve the detection accuracy of light squats, which present challenges due to their subtle, short-duration responses and typically a low percentage of appearance in ABA signals compared to healthy rails. Instead of using large network architecture, this work uses simple network architecture with no hidden layers to solve a complex spatiotemporal problem presented in early squat detection. The data used in this research contain four UCI benchmarks (Liver disorders, Breast cancer, Ionosphere, and Iris) and real-field ABA measurements from Dutch and Swedish railways. All implementations are done in MATLAB, where (.mat) files are analytical solutions and (.eps) and (.jpg) are figures used in the main manuscript.