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

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 accura...

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Bibliographic Details
Published in:PloS one 2019-12, Vol.14 (12), p.e0226751-e0226751
Main Authors: Yu, Xiaoying, Su, Hongsheng, Fan, Zeyuan, Dong, Yu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automation
Computer and Information Sciences
Computer simulation
Defects
Diameters
Education
Efficiency
Electrical engineering
Engineering and Technology
Gaussian process
Genetic algorithms
Laboratories
Machine learning
Maintenance
Methods
Monitoring systems
Physical Sciences
Position measurement
Predictions
Railroad wheels
Railroads
Research and Analysis Methods
Search algorithms
Sensors
Simulated annealing
Swarm intelligence
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0226751