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


Autoři: Xiaoying Yu aff001;  Hongsheng Su aff001;  Zeyuan Fan aff002;  Yu Dong aff001
Působiště autorů: College of Automation and Electrical Engineering & Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou, Gansu, China aff001;  School of Rail Transportation, Shandong Jiaotong University, Jinan, Shandong, China aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226751

Souhrn

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.

Klíčová slova:

Algorithms – Artificial intelligence – Covariance – Machine learning algorithms – Optimization – Simulated annealing – Wheels


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Článek vyšel v časopise

PLOS One


2019 Číslo 12