【作者】 卢磊；

【导师】 闫纪红；

【作者基本信息】 哈尔滨工业大学 ， 机械制造及其自动化， 2011， 硕士

【摘要】 工业自动化水平的提高对设备提出了早期故障诊断的需求。由于复杂的工作环境和现场噪声的干扰,设备的故障信号容易被噪声污染,如何有效的从低信噪比监测信号中提取弱故障特征成为故障诊断领域迫切需要解决的问题。结合故障信号自身的特点,运用弱信号特征提取方法和早期故障诊断理论解决机械故障诊断中信号降噪和弱特征加强等问题以及进行设备的早期故障诊断是当前机械故障诊断领域迫切需要研究的重要课题之一。本文研究了信号的降噪方法、设备故障的弱特征提取、早期故障诊断理论及其在轴承故障诊断中的应用。(1)采用小波分析的多分辨率算法,信号被分解为不同尺度上的低频分量和高频分量,信号中的微弱特征随着尺度因子的递进变化逐渐被放大。在每个分解尺度上,将变换系数与通过阈值规则设置的门限因子进行比较处理,实现了信号的降噪处理,提高了信噪比和加强了信号中弱特征。(2)基于信号的局部特征分析,经验模态分解采用包络分析理论将故障信号分解为频率从高到低变化的固有模态分量。针对经验模态分解过程的边界效应问题和传统延拓方法的缺陷,本文采用了基于奇异值分解和支持向量回归机的端点预测延拓方法。奇异谱分析方法能够有效地检测信号中的周期特征,为确定支持向量回归机延拓点的数目提供了一种可行的方法。通过阈值扫描法剔除了分解产生的“伪”分量,分析表明该方法能够有效地抑制边界效应和提取信号中的弱特征分量。(3)针对设备早期故障特征不明显、可分性差的特点,采用支持向量机建立最优分类决策模型。采用高斯核函数增加样本的可分性,实现了故障特征空间的映射变换。本文以轴承故障为研究对象,系统地研究了故障类别、训练样本数目和故障诊断精度之间的关系,分析表明该方法能够有效地解决小样本情况下故障诊断精度低的问题。(4)基于Matlab语言和VB环境的混合编程技术开发了信号的特征提取和设备的早期故障诊断可视化操作系统,提供了一个操作方便、可扩展性强的特征提取和早期故障诊断平台。更多还原

【Abstract】 The development of industrial automatization results in the increasing demand of early fault diagnosis. While the fault features may easily be contaminated for serious noise interference and complex working condition, how to detect weak fault features from low signal noise ratio (SNR) vbration signals becomes an urgent problem that need to be solved. Applying weak signal processing method and incipient fault diagnosis theory to conduct signal denoising and early fault diagnosis is a hot topic in mechanical fault diagnosis. This dissertation studied vibration signal processing, weak fault features detecting and incipient fault diagnosis method.(1) Vibration signal was decomposed into a set of low frequency parts and high frequency parts by adopoting the multi-resolution analysis method. Weak signal features at any point of the signal can be revealed with the changing of scale factor and translation factor. At each decomposition level, the wavelet coefficients were compared to the threshold that was preset according to threshold rules, and the denoising signal can be constructed by the new wavelet coefficients. Simulation results show that this method can effectively improve SNR of the orignal signal and reveal weak fault features.(2) Based on analysis local characteristics of the signal, vibration signal can be descomposed into a series of intrinsic mode functions (IMFs) ranging from high frequency to low frequency. Taking into account the defects of empirical mode decomposition (EMD) shifting processing and shortages of traditional extension methods, this dissertation proposed a novel methodology based on singular value decomposition (SVD) and support vector regression (SVR), periodic features can be detected effectively according to SVD, which provided an approach to determain signal extension length in SVR method. Meanwhile, a scanning method was employed to get rid of the redundant IMFs, simulation results show effectiveness of the proposed method.(3) Support vector machine (SVM) was employed to construct the optimal hyperplane, it can obtain the minimum structure risk and maximum generalization ability and outreach capacity. Meanwhile, introducing Gaussian kernel function, fault features can transform from low dimension space to high dimension space, which can significantly improve separability of training samples. This method was employed to conduct bearing fault diagnosis, realationship between fault categories, training samples and diagnostic accuracy was studied, results show that the proposed method can effectively identify early fault features even when the training samples are insufficient.(4) The dissertation developed a visual fault diagnosis system based on Matlab and Visual Basic, which provided an easy operation and high expansibility fault diagnosis tool.更多还原