基于神经网络的油井故障诊断系统研究与实现  

The research and implementation of neural network in fault diagnosis system of the oil deposit

作　　者：袁文琪[1] 李训铭[1] 

机构地区：[1]河海大学能源与电气学院,江苏南京210098

出　　处：《电子设计工程》2012年第8期35-37,共3页Electronic Design Engineering

摘　　要：油田的井下设备由于地下工况恶劣较容易发生故障,故及时准确地诊断出油井工况,对提高采油效率具有重要意义。本文通过对神经网络的概述,主要介绍了基于神经网络的诊断系统的设计思想和系统结构。并以有杆抽油系统故障诊断为实例验证,通过专家知识和经验、提取功图特征值,基于神经网络的故障诊断系统在具有较高的诊断效率和准确性。The underground equipments of oil deposit will easily break down due to bad underground working conditions.So diagnosing the working condition timely and accurately is very meaningful for increasing production efficiency.This text introduced the overview of the neural network.It mainly discussed about the design ideas and system structure of diagnosis system based on neural network.And at the same time,this text presented an example of fault diagnosis in the oil well rod pumping system based on the neural network by using expert knowledge and experience and extracting characteristic values of the indicating diagram.The diagnosis system has high diagnosing efficiency and correctness.

关 键 词：故障诊断 神经网络 有杆抽油系统 功图 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]