摘要
针对传感器在数据获取中的局限性和无法用于对实验系统进行全面的数据收集问题,对大流量止回阀实验系统的快速预测模型技术进行了研究,建立了实验系统的快速预测模型,进行了快速预测模型的结果分析。首先,搭建了实体模型,根据大流量止回阀实验系统的结构,结合实验系统的工作原理对其进行了简化,并进行了有限元分析;然后,利用快速预测模型的关键技术构建了实验系统数据库,进行了实验系统的样本采集;通过比对不同机器学习算法的预测准确率,选择了随机森林算法,并建立了实验系统内压与应力应变的关系;最后,进行了快速预测模型的结果分析,并完成了实验系统整体预测实验和实验系统部件单独预测实验。研究结果表明:采用随机森林(RF)算法建立的快速预测模型,拟合优度(R 2)达到了0.99,相较于深度神经网络(DNN)算法和梯度提升树(GBDT)算法,拟合优度(R 2)提高了68.97%和51.47%。实验系统整体预测与实验系统部件单独预测的对比试验结果表明:整体预测模型的预测速度更快,且精度可以达到97.43%。
Aiming at the limitations of sensors for data acquisition and the inability to collect comprehensive data for the experimental system,the fast prediction modeling technology of the high flow check valve experimental system was studied.The rapid prediction model of the experimental system was established,and the results of the rapid prediction model were analyzed.First of all,the solid model was constructed and based on the structure of the experimental system of high flow check valve.At the same time,the working principle of the experimental system was combined to simplify it and carry out finite element analysis.Then,the key technology of fast prediction model was used to construct the experimental system database and realize the sample collection of the experimental system.By comparing the prediction accuracy of different machine learning algorithms,the random forest(RF)algorithm was selected to establish the relationship between the internal pressure and stress-strain of the experimental system.Finally,the results of the fast prediction model were analyzed,and the overall prediction experiment of the experimental system and the individual prediction experiment of the experimental system components were done.The results show that the fast prediction model established by the random forest algorithm has a goodness of fit(R 2)of 0.99,which is 68.97%and 51.47%higher compared to the deep neural network(DNN)algorithm and gradient boosted tree(GBDT)algorithm.Comparison tests between the overall prediction of the experimental system and the individual prediction of the experimental system components show that the overall prediction model has a faster prediction speed and an accuracy of 97.43%.
作者
王江坤
赵晶
查洒洒
王剑
曲洺剑
张俊飞
WANG Jiangkun;ZHAO Jing;ZHA Sasa;WANG Jian;QU Mingjian;ZHANG Junfei(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,China;Shenyang Shengshi Wuhuan Technology Co.,Ltd.,Fushun 110172,China;Liaoning Wuhuan Special Materials and Intelligent Equipment Industrial Technology Research Institute,Shenyang 118015,China)
出处
《机电工程》
CAS
北大核心
2024年第4期659-665,共7页
Journal of Mechanical & Electrical Engineering
基金
辽宁省海洋经济发展项目(2022-47-1-09)。
关键词
大流量旋启式止回阀
单向阀
随机森林算法
响应时间
深度神经网络
梯度提升树
决定系数
large flow swing check valve
non-return valve
random forest(RF)algorithm
response time
deep neural network(DNN)
gradient boosting tree(GBDT)
coefficient of determination(R2)
