摘要
液膜密封泵送性能的直接测试较难实现,为了在线测得液膜密封的性能参数,提出了基于误差反向传播(BackPropagation,简称BP)神经网络的性能监测方法.首先,通过实验测得不同压力和转速下,液膜密封的泵送量和液膜厚度;其次,利用实验数据训练BP神经网络,采用“遍历输入量区间”的方法得到神经网络输出值,绘制模拟数据等值线图,并与实测数据等值线图进行比较,评价神经网络泛化性;然后,从泛化性、准确性和回归性3个方面比较了5种训练函数的非线性回归效果,得到最优BP神经网络模型;最后,对BP神经网络的监测效果进行检验.结果表明:trainbr函数具有泛化能力强、对隐含层节点数依赖性弱的特点,应用该训练函数的BP神经网络满足液膜密封的监测要求.
Direct monitoring of the pumping performance of liquid film seals is difficult to be put into effect.In order to test the performance parameters of the seals online,the monitoring method based on BP neural network is proposed..The pumpage and film thickness of liquid film seals under different pressures and revolving speeds are obtained through test firstly.The BP neural network is trained with experimental data.Output data of the network is gained by traversing input range method and the contourplots are drawn to be compared with the ones of measured data so as to assess the neural network generalization.Then,non-linear regression effects of five training functions are compared in three aspects involving generalization,accuracy and regression and the optimal BP neural network models are obtained.Finally,the monitoring effects of BP neural network are tested.And the results indicate that trainbr function is characterized by strong generalization and weak dependence on the number of hidden layer nodes.And BP neural network using trainbr function can satisfy the monitoring requirements for liquid film seals.
作者
朱晓琳
李勇凡
李振涛
郝木明
Zhu Xiaolin;Li Yongfan;Li Zhentao;Hao Muming(Inner Mongolia Radio&TV University,Hohhot,Inner Mongolia,010020;China Universityof Petroleum(East China),Qingdao,Shandong,266580)
出处
《石油化工设备技术》
CAS
2020年第6期55-61,I0005,I0006,共9页
Petrochemical Equipment Technology
关键词
BP神经网络
训练函数
液膜密封
泛化性
BP neural network
training func-tion
liquid film seal
generalization
作者简介
朱晓琳,女,2013年毕业于中国石油大学(华东)动力工程及工程热物理专业,硕士,主要从事流体动密封方面的研究工作,讲师.Email:zhuxiaolin19880708@163.com.
