Abstract
An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.
Highlights
With the rapid development of high-speed railways worldwide, the driving speeds of trains continue to increase [1,2]
If the measured value of train speed is less than the actual value, there is a risk of rear-end collision, which affects driving safety
It can be intuitively seen from the results that the predicted results of the Fast simulated annealing algorithm (FSA)-Gaussian process regression (GPR) algorithm used in this paper are closest to the actual measured values, while the predicted values of the other three algorithms are greatly deviated from the actual values
Summary
With the rapid development of high-speed railways worldwide, the driving speeds of trains continue to increase [1,2]. Train control systems must exhibit greater speed measurement accuracy than ever before. The speed and position information of a train are very important parameters to ensure normal operations of the train control system and safe operation of the train[3,4]. If the measured value of train speed is less than the actual value, there is a risk of rear-end collision, which affects driving safety. If the measured value of train speed is greater than the actual value, it leads to early braking and affects driving efficiency. Improving the measurement accuracy of the speed and position of a train is essential for driving safety and efficiency
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