A New Approach Based on Predictive Maintenance Using the Fuzzy Classifier in Pantograph-Catenary Systems

Abstract

Faults in railway and pantograph and catenary systems significantly endanger transport safety. Since periodic maintenance or maintenance at the time of fault will be costly, predictive maintenance methods are recommended to prevent faults in these systems. Performing predictive maintenance requires obtaining data from the railway and recording and using this data appropriately. The platform used in this study, allows data to be recorded from every device that can be connected to the internet. This recorded data are easily accessible. For this reason, this study proposes a new predictive maintenance method using the fuzzy classifier in railway systems. A simulation is performed using an internet of things platform. The data are recorded instantly on the proposed platform. Two modules, a camera and a temperature sensor, to be placed on either side of a rail line are simulated. Correlation is applied to the pantograph images obtained with the camera, and vector features are obtained from the images. In this way, a correlation coefficient for each image is calculated and gives information about the health of the pantograph. Data consisting of correlation coefficients and temperature values from modules is transmitted as input to a fuzzy classifier. The fuzzy classifier provides results about the health status of the pantograph. The results are evaluated by the ROC analysis method. When the results of the simulation are examined, it is shown that the proposed method produces effective and accurate results.