基于RBF神经网络的故障诊断-控制理论与控制工程专业论文.docxVIP

江苏大学硕士研究生毕业论文 摘 要 石油钻井是一种连续的作业过程，一旦发生故障将会带来严重的经济损失。 钻井设备属于大型设备，有许多传动机构，易发生故障的部件主要是传动滚动轴 承。本文针对油田钻井设备滚动轴承的故障诊断和预测进行了研究，可用于预防 事故的发生。 滚动轴承的振动信号是非平稳信号，用常规方法难以从振动信号中提取有效 故障特征信息。本文采用小波变换理论对滚动轴承振动信号进行数据分析处理， 结合RBF神经网络进行故障诊断，取得了良好的诊断效果。 由于在油田工地现场采集的振动信号数据中背景噪声很大，直接进行特征提 取，难以有效地将振动信号中的故障特征信息提取出来。本文分析总结了几种小 波消噪阈值的选取方法，采用小波消噪对振动信号进行消噪预处理，有效地消除 了背景噪声对滚动轴承振动信号的干扰，提高了信噪比。根据轴承振动信号??特 点，研究了小波熵从振动信号中提取故障信息的方法，并用该方法对现场采集的 三种工况下的振动信号数据进行了分析研究。结果表明，振动信号特征提取的小 波熵方法，能有效地将隐含在振动信号中的故障特征提取出来。 在特征提取的基础上，进一步采用k—means聚类算法的RBF神经网络，将 采用小波熵方法提取出来的特征向量作为RBF神经网络的输入，对所提取的特 征向量进行分类，从而得出轴承的工作状态。故障诊断统计结果表明，k—means 聚类算法的RBF神经网络故障诊断的正确率达到94．2％，取得了理想的诊断效 果。本文也采用了BP神经网络对该三种工况下的滚动轴承进行了故障诊断，通 过比较验证表明，RBF神经网络在油田钻井滚动轴承故障诊断中具有明显的优 势。 关键词：滚动轴承，故障诊断，小波熵，特征提取，RBF神经网络  江苏大学硕士研究生毕业论文 Abstract The producing process of petroleum drilling is a continuous process， if it goes wrong，the economic loss will be very seriously．The drilling equipment is one kind of large equipments，has many transmission mechanisms，and its roiling bearings easily go wrong．This paper studied the fault diagnosis and forecast of the drilling rolling bearings，and prevented faults from occurring by this method． The fault feature information can not be obtained availably by regular methods to extract the features of nonstationary vibration signals produced by fault rolling bearings．The paper used the principle of wavelet to analyze and process the vibration signal data of the rolling bearings，used RBF neural network to diagnose the faults，and the result of the fault diagnosis was very well． Because there was lots of background noise in the vibration signals of the rolling bearings，the real fault feature information could not be obtained availably if the features were exacted directly．The paper used the principle of wavelet denoise to preprocess the vibration signals，and several methods of selecting the thresholds of wavelet denoise were also introduced．The effect of anti—ground noise was perfect with the methods of wavelet denoise，and improved the