Optimization of Railway Track’s Preventive Maintenance Planning Based on Predicting the Increase in Maximum Defect Sizes (Vigral Method)

This article describes a solution for optimization of railway track’s preventive maintenance scheduling. Solution is based on predicting spontaneous increases of maximum defect sizes occurring between two consecutive track measurements. Predictions are made immediately after the latest measurement. The planning of preventive maintenance works is based on periodic measurements of geometric parameters’ values. Changes in the track condition manifest as an increase in the size of "large" defects and a decrease in "small" ones through superposition, and sometimes as an abrupt change in the maximum defect size. The process properties depend on the track's physical condition and the magnitude of train load. This process is associated both with changes in the size of existing defects and with the formation of new ones that exceed the existing ones in size. The abrupt appearance of defects exceeding the current maximum values, occurring at random times between measurements, significantly impacts the track's technical condition and must be considered when planning track maintenance works. Thus, optimizing preventive maintenance work requires obtaining information about the track condition in future immediately after each measurement. The problem of predicting the appearance of defects whose sizes exceed those recorded in the latest measurement has not been studied. Analysis has shown that this phenomenon occurs to varying degrees in 5-10% of track segments, when subsequent measurements register the appearance of new, larger defects that arose in the period between measurements. Information about the possible appearance of such defects allows optimization of the track maintenance process. The method for predicting changes in the track's technical condition described in this article allows, with high reliability, immediately after the latest measurement to predict the appearance, during interval before the next regular measurement, of defects whose sizes exceed the maximum recorded in the latest measurement. The method also allows identifying sudden spontaneous deterioration of the track. The method is based on analyzing the homogeneity (compactness) property of the values of the ISDF. This function shows the cumulative length of each-size track irregularity within a track segment. For classifying results and making decisions, the "nearest neighbor method" is used. The method has been tested for predicting track condition for future periods of 1, 2 and 3 months after the latest measurement. For surface defects, the probability of correctly predicting a spontaneous increase in maximum defect size is within 0.91-0.98 range, and the probability of false positives is between 0.03-0.09.