基于神经网络的两层分采同步抽油系统井下故障诊断技术  

Neural-Network-Based Down-Hole Fault Diagnosis Technique for Sucker-Rod Pumping System of Double-Layer Separate Recovery

作　　者：王金东[1] 蔡东升[2] 张嘉钟[1] 刘树林[1] 

机构地区：[1]哈尔滨工业大学航天学院,哈尔滨市150001 [2]大庆石油管理局三次采油装备成套公司,黑龙江省大庆市163453

出　　处：《中国机械工程》2002年第15期1302-1305,共4页China Mechanical Engineering

基　　金：黑龙江省教育厅科学技术研究项目 (10 5 1112 6)

摘　　要：建立了两层分采同步抽油系统的诊断模型 ,提出了求解这一非完整系统的迭代差分法 ,并由此建立了基于神经网络的两层分采同步抽油系统井下故障诊断专家系统。对专家系统各个模块的功能及其整个系统的运行机制进行了论述 ,阐明了 BP网络诊断模块的初始化、训练和测试过程 ,并采用带有自适应学习率和动量因子的改进 BP算法对其进行训练。借助功能强大的 MATLAB语言系统及其工具箱 ,完成了故障诊断专家系统软件的设计。The existing fault diagnosis technique of sucker-rod pumping system can not meet the needs to diagnose the faults of sucker-rod pumping system of double-layer separate recovery. In this paper, the diagnostic model of sucker-rod pumping system of double-layer separate recovery is established, an iterative difference method of the noncomplete system is proposed, and then a neural-network-based down-hole fault diagnosis expert system of sucker-rod pumping system of double-layer separate recovery is built. The function of various blocks and the working principle of expert system is discussed, the initialization, training and testing process of BP network is illustrated, and the BP network is trained with the improved BP algorithm that has self-adapting learning rate and momentum factor. The software of the fault diagnosis expert system has been designed with the aid of the MATLAB and its Toolbox.

关 键 词：同步抽油系统 分层采油 空心泵 故障诊断 BP网络 

分 类 号：TE93[石油与天然气工程—石油机械设备] TE355.5