Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm

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.

Reliability analysis and data driven modelling of railway component failure

“…The paper concluded that Gaussian Process Regression performs better than Support Vector Machines and Artificial Neural Networks in the accuracy of their tool wear predictive model because of the Gaussian distributed noise data can be utterly modeled quantitatively. Yu et al [171] applied a GPR model to inspect the dynamic change patterns in wheel diameter with the operation mileage increase based on a small group artificial measurement data. A normal distributed wheel wear rate regarding to the driving distance was assumed in that paper.…”

“…For example, the rail head surface height decrease due to wear is nonlinearly related to operation load, rail density and the sliding distance [57]. The wheel and rail wear rate shows a normal distribution and there is nonlinear mathematical relation between wear amount and the train driving distance [58]. Zhou et al [59] found that the wear rate keeps constant for a broad ranges of MGT (Million Gross Ton) but suddenly changes at specific MGT which suggests that the wear rate is not a linear function of the cumulative load.…”