摘要随着石油业的飞速发展和石油开发的迫切需要，应用机械采油方法提高油田采油速度和最终采收率，是现代石油开采的一项重要举措，然而潜油电泵机组结构比较复杂，工作环境极其恶劣，在使用过程中发生故障的概率较高，严重时会出现机组落井的重大事故，油井报废，造成经济损失，所以说研究潜油电泵的偏磨具有重大的意义。本文研究的课题是潜油电泵偏磨诊断特征参数的选择和分析，通过学习模式识别的相关理论和知识，从时域、频域、时频域或时差域进行特征参数的提取，例如熵、傅里叶函数、小波系数、均值等参数，然后使用基于距离的可分性判据的方法，对特征的分类能力进行评价分析，运用Matlab软件，编程实现，接着完成分类能力判定的程序设定，对结果进行分析，最后完成潜油电泵偏磨的特征参数提取和分类能力鉴定。49483

毕业论文关键词：潜油电泵；模式识别；参数提取；

 Abstract With an urgent need for the rapid development of the oil industry and oil development, the application of mechanical recovery methods to improve oil extraction rate and ultimate recovery is an important measure of the modern oil exploration, however, the submersible pump structure is more complex, the work environmentextremely poor, in the course of a high probability of failure in severe cases the units fall into a major accident, an oil scrapped, causing economic losses, so research submersible pump eccentric wear of great significance. The subject of this paper is a submersible pump eccentric wear of the diagnosis of characteristic parameters of selection and analysis, through the study of pattern recognition theory and knowledge from the time domain, frequency domain, frequency domain or time difference domain characteristic parameter extraction, such as entropy Fourier function, wavelet coefficients, the mean parameters, and then use the separability criterion of the distance-based approach, the classification capability of the characteristics evaluated and analyzed using Matlab software, programming, and then complete the classification ability to determine the procedures set that depends on the analysis of the results, the finalization of the submersible pump eccentric wear of the feature extraction and classification ability identification.

Keywords: Electric submersible pump; pattern recognition; parameter extraction

目  录

第一章 绪论 1

1.1 课题背景与研究意义 1

1.2 潜油电泵机组概述 1

1.3 课题研究现状 4

1.3.1 潜油电泵机组故障分析及诊断研究现状 4

1.3.2 振动测试在旋转机械设备故障诊断中的研究现状 5

1.3.3 振动检测在潜油电泵机组故障诊断中的应用现状 7

1.4 模式识别的相关理论和知识 8

1.4.1模式识别方法 9

1.4.2 模式识别的应用 11

1.4.3 特征参数与模式识别的相关性关系 12

1.5 本章小结 12

第二章 潜油电泵特征参数分析 14

2.1 潜油电泵机组偏磨的力学分析 14

2.2 时域分析 17

2.2.1 时域波形分析 17

2.3 幅值分析 18

2.4 熵参数的提取