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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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226751&type=printable |
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| author | Xiaoying Yu Hongsheng Su Zeyuan Fan Yu Dong |
| author_facet | Xiaoying Yu Hongsheng Su Zeyuan Fan Yu Dong |
| author_sort | Xiaoying Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3a28d1bb3d2e43fd8a3257d19db9c456 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3a28d1bb3d2e43fd8a3257d19db9c4562025-08-20T02:17:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011412e022675110.1371/journal.pone.0226751Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm.Xiaoying YuHongsheng SuZeyuan FanYu DongAn 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226751&type=printable |
| spellingShingle | Xiaoying Yu Hongsheng Su Zeyuan Fan Yu Dong Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. PLoS ONE |
| title | Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. |
| title_full | Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. |
| title_fullStr | Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. |
| title_full_unstemmed | Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. |
| title_short | Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. |
| title_sort | prediction of train wheel diameter based on gaussian process regression optimized using a fast simulated annealing algorithm |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226751&type=printable |
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