A Semi-Supervised Diffusion-Based Paradigm for Vehicle-Track System Health Monitoring With Distributed Acoustic Sensing

Monitoring the health of vehicle-track system using deep learning and distributed fiber optic sensing presents a significant challenge due to the vast volume of real-time data and the difficulty of directly assessing the system’s condition. This often results in a severe imbalance in the distribution of extreme samples within the dataset, as large-scale signal collection typically lacks manual labeling. Consequently, supervised deep learning models face limitations due to insufficient labeled training data, while unsupervised deep learning models struggle with contamination from ambiguous samples whose health status remains unclear, hindering the development of robust and accurate models. To address this challenge, we propose SemAnoDiffusion, a semi-supervised model based on blur diffusion and an enhanced contrastive loss training approach. SemAnoDiffusion leverages a small set of labeled data alongside a large amount of unlabeled samples to accurately differentiate between anomalous data, normal data, and ambiguous samples that fall between these categories. In a case study of a metro system in Singapore, Distributed Acoustic Sensing and accelerometer arrays were used to collect track vibration responses as trains passed, with wheel flats occurring in a small subset of the trains. SemAnoDiffusion achieved 100% accuracy in classifying manually labeled normal and anomalous samples and effectively identified semi-damaged samples with unclear damage levels from the labeled data, successfully detecting all trains with wheel flats.