Optimized Railway Track Condition Monitoring and Derailment Prevention System Supported by Cloud Technology

Abstract

This paper describes an easy way to monitor railway track abnormalities and update information on the track’s status to the cloud. Abnormalities present in railway tracks should be identified promptly and rectified to ensure safe and smooth travel. In this paper, a cloud-based track monitoring system (CTMS) is proposed for the monitoring of track conditions. The micro-electro mechanical systems (MEMS) accelerometers which are mounted in the axle are used to measure the railway track abnormality. The measured signal is optimized using the flower pollination optimization algorithm (FPOA). Because of signaling problems in the global positioning system (GPS), it is difficult to estimate the exact location of the abnormality in real time. A new method is introduced to overcome this problem. It provides the location of an abnormality even when the GPS signal is absent. The performance of the CTMS is compared with three different speed scenarios of the vehicle. The information about the abnormality on the track can be shared with other trains that pass through the same location so that the driver can reduce speed in that location to avoid derailment. Finally, an experimental setup was developed and the performance of CTMS is studied under four different irregularity cases.