Wheels are a critical component of railway infrastructure and work as the load carrier of the train. However, defective wheels pose a serious risk to safety that can gravely jeopardize people's safety. There is a significant risk of injury or death from defective wheels, endangering the lives of individuals. In this research, FaultSeg dataset is presented for automatic train wheel defect detection for railway transportation around the world. The FaultSeg consists of 829 manually annotated images of faulty wheels acquired by an indigenously developed wayside data acquisition system. Expert Annotators have manually annotated three classes of potential defects: Cracks/Scratches, Shelling, and Discoloration. To assess the practicality of the FaultSeg dataset for training and testing advanced deep learning (DL) models, the dataset was used to train and evaluate the YOLOv9 instance segmentation algorithm. The model achieves an approximate score of 87% accuracy. These results showcase the usability of the FaultSeg dataset in automatic inspection systems and data driven predictive maintenance strategies to safeguard and ensure the safety of railway transportation.