Perpustakaan Digital ITB

This thesis addresses the crucial issue of detecting track conditions, including vertical, lateral, and combined track conditions, which profoundly affect railway safety and passenger comfort, by utilizing vehicle dynamic responses. Traditional track maintenance methodologies, primarily manual inspections and using specialized monitoring vehicles, are constrained by their inspection frequency. Employing models that correlate vehicle dynamic responses with track conditions could significantly enhance fault detection, particularly when these models are integrated into commercial vehicles for near-real-time monitoring. In this study, we investigate the application of deep learning for classifying track conditions using vehicle dynamic responses. Our approach employs a variant of convolutional neural networks (ConvNets), specifically GoogleNet, to detect track conditions. Additionally, we use the Gramian Angular Summation Field (GASF) to transform carbody acceleration from a temporal representation to a twodimensional image. To address the issue of dataset imbalance that inherently occurs in real-world data, we employ several techniques, including cross-validation and image augmentation. The image augmentation approach can be applied to time-series data, such as vehicle dynamic responses, by leveraging the advantages of the GASF transformation.