Temperature compensation method based on amcpso optimized kriging interpolation

被引：0
|
作者：
Zhang S. [1 ]
Wang D. [1 ]
Huang C. [2 ]
Chen X. [1 ]
Zheng X. [1 ]
Liu M. [1 ]
机构：
[1] School of Mechanical Engineering, Dalian University of Technology, Dalian
[2] Xi’an Railway Signal Co., Ltd, Xi’an
来源：
Journal of Railway Science and Engineering | 2024年 / 21卷 / 01期
关键词：
Adaptive mutation chaotic particle swarm optimization (AMCPSO); Calibration experiment; Conversion force sensor; Kriging interpolation; Temperature compensation;
D O I：
10.19713/j.cnki.43-1423/u.T20230332
中图分类号：
学科分类号：
摘要：
In order to reduce the influence of temperature variation on the measurement accuracy of the conversion force sensor, a temperature compensation algorithm (AMCPSO-Kriging) with an adaptive variational chaotic particle swarm algorithm (AMCPSO) optimized for Kriging interpolation was proposed. The conversion force sensor was developed, the effect of temperature on the sensor output was analyzed, and a temperature compensation calibration experimental platform was established. The sample set required to build the temperature compensation model was obtained through the calibration experiment, and the sample data was optimized by the data sparseness method. The temperature compensation model was constructed by Kriging interpolation. The AMCPSO algorithm was employed to search for the optimal solution for the range parameter θ and the smoothness parameter pk in Kriging interpolation by using the root-mean-square error sum of the model prediction as the fitness function under the cross-validation method to obtain the temperature compensation model with the best performance. Based on the AMCPSO-Kriging temperature compensation model, the measurement effect of the converted force sensor was experimentally verified and compared with the standard force sensor. It is found that the average running time of the algorithm is decreased from 1 076 s to 6 s by sparsifying of the sample data, and thus improving the operating efficiency of the temperature compensation algorithm. From -20 ℃ to 70 ℃, the Kriging model optimized by AMCPSO algorithm effectively improved the measurement accuracy of the conversion force sensor. Meanwhile, the average full-scale error of the conversion force sensor measurement is decreased from 1.2%FS to 0.6%FS compared with the Kriging interpolation without AMCPSO algorithm. The effect of temperature compensation is proved through the field experiments. The absolute error of the conversion force sensor is within 70 N, while the maximum full range error is found to be 2.3%FS. The proposed temperature compensation method effectively eliminates the influence of temperature on the measurement accuracy of the sensor and meets the requirements of railroad working conditions, which is of great value for the practical application of the conversion force sensor on the railroad. © 2024, Central South University Press. All rights reserved.
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页码：342 / 353
页数：11
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