传感技术学报2017，Vol.30Issue(11)：1766-1775,10.DOI:10.3969/j.issn.1004-1699.2017.11.025
基于混合优化算法的销轴传感器温度补偿及应用
Temperature Compensation and Application of Pin Shaft Sensor Based on Hybrid Optimization Algorithm
摘要
Abstract
Aiming at the measuring precision will be decreased due to the temperature drift of the strain-gage pin sensor resulted from the temperature variation during the underground working process,a temperature compensation model of the RBF neural network optimized by the drosophila algorithm is proposed. The extended parameters of the neural network are globally optimized by employing the drosophila algorithm,the parameters are measured by using the strain test platform,and the temperature compensation model is trained by employing the nonlinear mapping ca-pability of the neural network. To validate the compensation effect and the training efficiency of the temperature compensation model,the test is performed by using the sensor under 35 ℃. The result shows that the average error of the temperature compensation model is far less than that of the single algorithm compensation,the method is of the high training efficiency and the good compensation effect,the measuring precision of the sensor can be increased under the different temperatures and the different loads. The model of the paper is employed in the shearer working process,and the forces of the guiding sliding boots during the cutting process of the moving shearer are obtained. The research result of the paper provides the basis for the structure optimization of the guiding sliding boots,the shearer reliability improvement and the lifetime of the shearer.关键词
果蝇算法/RBF神经网络/销轴传感器/温度补偿/应变片Key words
drosophila algorithm/RBF neural network/pin sensor/temperature compensation/strain gage分类
信息技术与安全科学引用本文复制引用
陈洪月,张坤,王鑫,李恩东,宋秋爽..基于混合优化算法的销轴传感器温度补偿及应用[J].传感技术学报,2017,30(11):1766-1775,10.基金项目
国家自然基金项目(514041325,511774162) （514041325,511774162）
国家能源研发(实验)中心重大项目(2010215) （实验）
辽宁省教育厅项目(L2012118) （L2012118）
辽宁省教育厅创新团队项目(LT2013009) （LT2013009）
