Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method

Abstract

It is crucial for automatic ticket gates (ATGs) on railways, also known as fare collection systems, to detect anomalies at an early stage, especially in the automatic separation module for multiple tickets. It is also required for efficient and low-cost monitoring without any additional sensors especially for old-type ATGs that need to be maintained frequently. However, the failure rate is basically very low, and monitoring data contain various kinds of normal status indicators depending on complicated mechatronics controls. In addition, it is hard to collect high quality learning data because ATGs are affected by various ticket conditions or timing when releasing tickets by users, which makes detecting anomaly signs difficult. For these reasons, conventional machine learning or deep learning methods are not suitable for anomaly detection for ATGs. In this paper, we propose a simple anomaly detection method with new anomaly sign index, called the histogram limitation method (HLM), for effective monitoring to realize preventive maintenance of ATGs based only on system log data. Despite being a quite simple and compact method, HLM provides anomaly sign scores that agree adequately with assessments by maintenance service engineers in our evaluation with real field ATGs in operation.