Defect Detection and Classification of Railway Track System for In-Service MRT in Tropical Regions Using a Contactless TBMS and Adaptive-DBSCAN

Railway track systems serve as vital parts of urban mobility and intelligent transportation. Detecting defects in rail track systems not only avoids unexpected downtime but also safeguards passenger lives. Moreover, defect classification holds great economic value, which optimizes both traffic operations and management strategies. Compared to lab tests and track recording vehicle-based field tests, defect detection and classification using in-service trains is an emerging area of study. This enables continuous monitoring, increases carrying capacity, and reduces maintenance costs, but it also requires robust performance and compatibility with various weather conditions. Considering the precipitation characteristics in tropical regions, this paper proposes a novel online defect detection and classification method for mass rapid transit (MRT) railway track systems by integrating a non-contact train-borne monitoring system (TBMS) and an adaptive density-based spatial clustering of applications with noise (Adaptive-DBSCAN) algorithm. The TBMS is developed based on the inductive coupling theory, affirming real-time, contactless, and effective defect detection in tropical regions with high annual and intense short-duration rainfall. By assessing the voltage health ratio (VHR) of the train-rail electrical path, the TBMS can simultaneously monitor defects from rail, ballast, and sleepers/ fasteners. To classify the group of each defect for maintenance decisions, Adaptive-DBSCAN is applied using VHR as inputs and calibrates the algorithm parameters adaptively. Therefore, it avoids the exhaustive traversal typically needed for parameter selection in DBSCAN while preserving accuracy. Experiments conducted on an in-service MRT train (operating at 80 km/h) verified the effectiveness of the proposed method.