Prediction of track irregularity based on improved non-equal interval grey model and PSO optimization

The research of track irregularity prediction using track dynamic detection data can be used to guide the railway track preventive maintenance work. This paper combined the improved non-equal interval grey model and particle swarm optimization (PSO) to achieve high-precision prediction of track quality index (TQI). Considering the characteristics of TQI dynamic inspection data, the improved non-equal interval grey model was proposed with the raw data smoothing optimization, cumulative initial value optimization and background value optimization. The smooth optimization parameters, initial value optimization parameter and background value optimization parameter were set as the search objectives. The prediction average relative error as the fitness function, the prediction model parameters adaptive optimization was performed using the heuristic search advantage of PSO algorithm. Based on the optimization parameters, the TQI prediction results of the fitting interval and extrapolation interval were calculated. The proposed method was verified with the measured TQI data of the up line of Hukun railway, and the prediction results were also compared with the existing TQI combination prediction models. The results show that the model can capture the random fluctuation and real-time evolution trend in TQI series. The average relative errors of the extrapolation interval are 2.04% and 2.54% respectively. The prediction performance is excellent. When there is significant oscillation for TQI sequences, the reliability of prediction results can be guaranteed by the model. Compared with the combined prediction models, the model avoids some unnecessary steps such as residual correction and multi algorithm fusion, and improves the prediction accuracy through limited optimization steps, which provides a new way for track irregularity prediction. © 2023, Central South University Press. All rights reserved.