【作者】 李媛媛；

【导师】 向玲；

【作者基本信息】 华北电力大学 ， 机械工程（专业学位）， 2016， 硕士

【摘要】 转子、齿轮和轴承等作为许多机械设备的重要零部件,在运转过程中起着举足轻重的作用,对这些零部件的故障诊断方法进行研究具有重要的现实意义。目前,通常以时频分析方法作为处理非平稳机械故障振动信号的主要手段。本文采用经验小波变换这种自适应的时频分析方法,并结合一些其他的处理手段,对机械故障振动信号进行分析处理。主要研究内容如下:1、基于经验小波变换时频分析的机械故障诊断经验小波变换(EWT)是一种针对信号傅里叶频谱进行划分滤波的信号处理方法。该方法首先针对信号的傅里叶频谱极大值进行自适应的划分;然后建立小波滤波器组对划分过的频谱进行滤波,对滤波后得到的单分量成分进行Hilbert变换并得到其时频分布;最后针对仿真信号和几组典型的实验转子故障信号进行EWT方法和经验模态分解方法(EMD)的性能比较研究,结果表明:EWT方法能准确地分析机械故障信号,故障特征值明显,可有效应用于旋转机械故障诊断。2、基于改进的EWT和快速谱峭度滤波的齿轮与滚动轴承故障诊断改进的经验小波变换方法(IEWT)是一种自适应信号处理方法,本文提出将该方法和快速谱峭度(FSK)相结合,进行齿轮和滚动轴承的故障诊断。首先采用IEWT方法对信号进行分解重构;然后对重构信号进行快速谱峭度滤波;最后对滤波后的信号进行包络谱分析,从而得到信号的故障特征频率成分。使用该方法分析齿轮断齿故障和滚动轴承故障信号,并与EMD方法的性能进行比较研究,结果表明该种方法更具区分性,可以有效识别齿轮和滚动轴承的的故障类型。3、基于IEWT和模糊C均值聚类的滚动轴承故障诊断将IEWT变换、奇异值分解(SVD)和模糊C均值聚类算法(FCM)相结合进行滚动轴承故障模式识别,该方法首先采用IEWT方法对信号进行分解,取相关系数较大的几个分量组成初始特征向量矩阵,而后对初始特征向量矩阵进行奇异值分解,组成奇异值特征向量矩阵,最后将奇异值特征向量矩阵作为数据源输入FCM进行故障模式识别。将该方法与基于EMD和FCM的模式识别方法进行对比,结果表明基于IEWT和FCM的模式识别方法具有更高的准确性和区分性,可以有效的应用于滚动轴承故障诊断。更多还原

【Abstract】 As the main components of machinery,rotor, gears and bearings is play a decisive role.Therefore, the fault diagnosis method research of these components is of great significant.At present, the time-frequency analysis method for processing non-stationary machinery fault vibration signal is the main method. This paper uses Experience Wavelet Transformthis which is a new kind of signal processing method,combined with some other means of signal processing method for machinery fault vibration signal processing.The main contents are as follows:1 Time-frequency Analysis Based on Empirical Wavelet Transform in Fault Diagnosis of MachineryEmpirical Wavelet Transform(Empirical Wavelet Transform, EWT) is an adaptive signal processing method for Dividing and filtering the signal spectrum.At first, the Fourier spectrum maximum of the signal is divided adaptively,then establishing a set of wavelet filter bank to filter the divided spectrumin and get a group of single component ingredients. Hilbert transform every single component ingredients you can get the instantaneous frequency and instantaneous amplitude. At last applied this method to the fault diagnosis of rotating machinery,in order to verify the effectiveness of the method for simulation of signal and several groups of typical experimental rotor fault signal, this paper compares the performance of the EWT method and the Empirical Mode Decomposition method.The result shows that:the EWT method can accurately analyze mechanical fault signal and the failure eigenvalues are evident.It can be effectively applied to rotating machinery failure.2 The gear and rolling bearing fault diagnosis based on improved Empirical Wavelet Transform and fast spectral kurtosis filteringImproved Empirical Wavelet Transform(IEWT) is a new kind of self-adaptation method of signal analysis. Combined this method with Fast Spectral Kurtosis filtering,we can achieved the purpose of gear and rolling bearing fault diagnosis. At first, the signal is decomposed using the IEWT method, and the two components which have the most obvious fault characteristics are extracted. Then, the reconstructed signal is filtered using the Fast Spectral Kurtosis filtering method. In the end, the filtered signal is analyzed by spectral envelope method and the fault features of signal are extracted. By analyzing the gear teeth broken and rolling bearing fault signals, it is indicated that the method based on IEWT is more distinctive than the method based on EMD. It can effectively identify the fault types of gear and rolling bearing.3 Roller Bearing Fault Diagnosis Based on IEWT, SVD and FCMA method for mechanical fault diagnosis based on IEWT, SVD and FCM is proposed.Firstly, fault signal is decomposed by IEWT, taking several component which have higher correlation coefficient to make up the initial feature vector matrix.Then decompose the initial feature vector matrix to obtain singular values to make up the singular value feature vector matrix.Finally, input the singular value feature vector matrix to the FCM to diagnosis the fault mode.The method is applied to recognise the rolling bearing fault pattern. Compared with the method based on EMD and FCM, it is more accurate and distinguishable. So it can be effectively applied to rotating machinery failure.更多还原