EMPIRICAL ANALYSIS OF TRACK IRREGULARITY ANOMALY DETECTION MODEL BY USING CLUSTERING METHOD

Abstract

When a train repeatedly runs on a track, the track irregularity, which is the distortion of the track, grad-ually increases due to load. Normally, the track irregularity is inspected periodically, and maintenance is performed when large track irregularity is detected. However, in rare cases, the track irregularity may pro-gress locally and rapidly. In order to ensure the safety of train operation, preventive maintenance is required to detect the signs of such rapid progress and to perform maintenance before it occurs. In this study, we have developed a model to identify in advance where large track irregularities are likely to occur by apply-ing cluster analysis to historical data of track irregularity and maintenance records. In addition, by applying this model to each track inspection and comparing it with the result of the previous track inspections, we showed the possibility of detecting sections of the track that require attention in advance. Specifically, the model extracts the section that have approached the center of the cluster requiring attention and the section that have moved between clusters.