Research data supporting chapter 'SNN with Time-Varying Weights for Rail Squat Detection' of Dissertation 'AI Solutions for Maintenance Decision Support in Railway Infrastructure'
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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.
Wassamon Phusakulkajorn 整理并上传至4TU.ResearchData的本数据集及配套代码，用于支撑其博士论文第3章（基于时变权重脉冲神经网络（Spiking Neural Network, SNN）的轨道压溃检测）的研究结果。该章节已以Phusakulkajorn, W., Hendriks, J.M., Li, Z., Núñez, A. 所著的《基于时变权重脉冲神经网络的轨道压溃检测》一文提交发表。本研究构建了一种基于时变权重的脉冲神经网络，旨在通过ABA测量数据检测不同严重程度的轨道表面缺陷，例如轨道压溃缺陷。本方法旨在提升轻度轨道压溃的检测准确率——此类缺陷因响应特征微弱、持续时间短，且相较于健康轨道，其在ABA信号中的占比极低，故检测难度较高。相较于复杂的大型网络架构，本研究采用无隐藏层的极简网络结构，以解决早期轨道压溃检测中存在的复杂时空问题。本研究使用的数据集包含四项UCI基准数据集（肝病数据集、乳腺癌数据集、电离层数据集与鸢尾花数据集），以及来自荷兰与瑞典铁路的现场实测ABA测量数据。所有实验实现均基于MATLAB完成，其中.mat格式文件为解析解文件，.eps与.jpg格式文件为主文稿中使用的配图。
