Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (4): 215-217.DOI: 10.3778/j.issn.1002-8331.2009.04.062

• 工程与应用 • Previous Articles     Next Articles

Intelligent dynamic diagnosis method and its application in identification of indicator diagram

ZHANG Qiang,XU Shao-hua   

  1. School of Computer and Information Technology,Daqing Petroleum Institute,Daqing,Heilongjiang 163318,China
  • Received:2008-09-23 Revised:2008-12-01 Online:2009-02-01 Published:2009-02-01
  • Contact: ZHANG Qiang


张 强,许少华   

  1. 大庆石油学院 计算机与信息技术学院,黑龙江 大庆 163318
  • 通讯作者: 张 强

Abstract: Aiming at the pattern diagnosis problem of the indicator diagram for pumping wells,a dynamic diagnosis method based on process neural networks is presented in this paper.The inputs and connection weights of Process Neural Networks(PNN) can be time-varying functions.The process pattern characteristics of the set for time-varying functions can be extracted automatically by the learning of the sample set for functions,and the combination of several process pattern characteristics can generate the output types.The method on mechanism has good adaptability to solve classification problems of time-varying signals.Combined with gradient descent algorithm,a dynamic diagnosis model based on PNN is built and the learning algorithm based on function basis expansion is given,which are applied to identify the working states of the indicator diagram for the observed pumping wells and show good effects.

Key words: indicator diagram, dynamic diagnosis, Process Neural Networks(PNN), learning algorithm, application

摘要: 针对抽油机井示功图模式诊断问题,提出了一种基于过程神经元网络的动态诊断模型和方法。过程神经元网络(PNN)的输入和连接权均可以是时变函数,通过对训练函数样本集的学习,可自动抽取时变函数样本的过程模式特征,并可将多个过程特征加以组合形成类别输出,在机制上对时变信号的分类问题具有较好的适应性。建立了一种基于PNN的动态诊断模型和方法,给出了基于函数基展开结合梯度下降的学习算法,对油田实测的抽油机井示功图进行工作状态识别,取得了较好的应用效果。

关键词: 示功图, 动态诊断, 过程神经网络, 学习算法, 应用