石油化工设备技术 ›› 2020, Vol. 41 ›› Issue (6): 55-61.doi: 10.3969/j.issn.1006-8805.2020.06.012

• 状态监测与分析 • 上一篇    下一篇

基于BP神经网络的液膜密封监测方法

朱晓琳1,李勇凡2,李振涛2,郝木明2   

  1. 1. 内蒙古广播电视大学,内蒙古 呼和浩特 010020;
    2. 中国石油大学(华东),山东 青岛 266580
  • 收稿日期:2019-11-25 接受日期:2020-10-25 出版日期:2020-11-23 发布日期:2020-11-23
  • 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

Monitoring Method for Liquid Film Seals Based on BP Neural Network

Zhu Xiaolin1, Li Yongfan2, Li Zhentao2, Hao Muming2   

  1. 1. Inner Mongolia Radio & TV University, Hohhot, Inner Mongolia, 010020;
    2. China University of Petroleum (East China), Qingdao, Shandong, 266580
  • Received:2019-11-25 Accepted:2020-10-25 Online:2020-11-23 Published:2020-11-23

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(Back Propagation, 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。

关键词: BP神经网络, 训练函数, 液膜密封, 泛化性

Abstract: 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 contour plots 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.

Key words: BP neural network, training function, liquid film seal, generalization