基于多小波分析的滚动轴承故障诊断方法研究
发布时间：2018-08-04 10:12
【摘要】：多小波又称向量小波,是在传统小波分析的基础上发展起来的一种新的小波构造理论。它是由多个小波基函数张成的函数空间,能够同时具备对称性、正交性、紧支撑性和高阶消失矩等特性,因而在模式识别、信号除噪方面比单小波分析更具优势。本文将多小波分析引入滚动轴承的故障诊断,研究内容如下: (1)为了使一维振动信号与多小波的r维空间相匹配,本文首先围绕信号的预处理方法展开讨论。以GHM多小波和CL多小波为研究对象,以信号重构误差和低频平均能量比为评价标准,分别针对仿真信号和实测振动信号,确立了最佳的多小波函数及相应的预处理方法。 (2)基于GHM多小波及系数重复行的预处理方式,研究了故障信号的降噪算法。为了避免阈值选择对降噪效果的干扰,本文分别将自适应阈值和奇异值分解技术与多小波分析相结合。通过轴承故障的仿真信号、实验信号以及工程数据的分析验证,表明多小波的降噪效果明显优于单一小波,而且更易于识别滚动轴承的早期故障特征。 (3)为了充分利用多小波分解后的各子带信息,本文研究了基于多小波包的故障诊断方法。以Shannon熵为代价函数,完成了对多小波包最优基的搜索和识别。在此基础上,将其与自适应阈值和奇异值分解的降噪方法相结合,利用不同故障模式(改变点蚀大小)下的轴承振动信号验证了降噪方法的有效性。 (4)针对正常轴承以及0.5-5mm的内、外圈点蚀共9类分析模式,构造了故障程度识别因子。其中,以多小波包分解和谱峭度分析作为特征提取的主要方法,进而结合特征系数的复杂度计算,完成了9种状态的模式识别,从中可以发现内、外圈故障不同的演变趋势。 (5)基于理论研究,利用LabVIEW与Matlab联合开发了一套融合多小波分析方法的故障监测与诊断系统,通过6307滚动轴承的模拟故障实验,验证了该系统的实用性及可靠性。
[Abstract]:Multi-wavelet, also called vector wavelet, is a new wavelet construction theory developed on the basis of traditional wavelet analysis. It is a function space of multiple wavelet basis functions (Zhang Cheng), which can simultaneously possess the properties of symmetry, orthogonality, compactness and high order vanishing moments, so it has more advantages in pattern recognition and signal denoising than single wavelet analysis. In this paper, multi-wavelet analysis is introduced into the fault diagnosis of rolling bearings. The research contents are as follows: (1) in order to match the one-dimensional vibration signal with the r-dimensional space of multi-wavelet, the preprocessing method of the signal is discussed in this paper. Taking GHM multiwavelets and CL multiwavelets as research objects and taking signal reconstruction error and low frequency mean energy ratio as evaluation criteria, the simulation signals and the measured vibration signals are respectively studied. The optimal multi-wavelet function and the corresponding preprocessing method are established. (2) based on the GHM multi-small sweep coefficient repeated row preprocessing method, the noise reduction algorithm of fault signal is studied. In order to avoid the interference of threshold selection to the noise reduction effect, the adaptive threshold and singular value decomposition (SVD) techniques are combined with multiwavelet analysis in this paper. Through the analysis of simulation signal, experimental signal and engineering data of bearing fault, it is shown that the denoising effect of multi-wavelet is better than that of single wavelet. Moreover, it is easier to identify the early fault features of rolling bearings. (3) in order to make full use of the information of each sub-band after multi-wavelet decomposition, a fault diagnosis method based on multi-wavelet packet is studied in this paper. With Shannon entropy as the cost function, the search and recognition of the optimal basis of multi-wavelet packets are completed. On this basis, it is combined with adaptive threshold and singular value decomposition to reduce noise. The vibration signals of bearing under different fault modes (changing the size of pitting) are used to verify the effectiveness of the noise reduction method. (4) for the normal bearing and 0.5-5mm, there are 9 kinds of analysis modes of external ring pitting. The fault degree identification factor is constructed. Among them, multi-wavelet packet decomposition and spectral kurtosis analysis are used as the main methods of feature extraction, and then combined with the complexity calculation of the feature coefficients, the pattern recognition of 9 states is completed. (5) based on the theoretical research, a fault monitoring and diagnosis system based on LabVIEW and Matlab is developed, which is based on the simulation of 6307 rolling bearing. The practicability and reliability of the system are verified.
【学位授予单位】：北京工业大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH133.33;TH165.3
本文编号：2163577
[Abstract]:Multi-wavelet, also called vector wavelet, is a new wavelet construction theory developed on the basis of traditional wavelet analysis. It is a function space of multiple wavelet basis functions (Zhang Cheng), which can simultaneously possess the properties of symmetry, orthogonality, compactness and high order vanishing moments, so it has more advantages in pattern recognition and signal denoising than single wavelet analysis. In this paper, multi-wavelet analysis is introduced into the fault diagnosis of rolling bearings. The research contents are as follows: (1) in order to match the one-dimensional vibration signal with the r-dimensional space of multi-wavelet, the preprocessing method of the signal is discussed in this paper. Taking GHM multiwavelets and CL multiwavelets as research objects and taking signal reconstruction error and low frequency mean energy ratio as evaluation criteria, the simulation signals and the measured vibration signals are respectively studied. The optimal multi-wavelet function and the corresponding preprocessing method are established. (2) based on the GHM multi-small sweep coefficient repeated row preprocessing method, the noise reduction algorithm of fault signal is studied. In order to avoid the interference of threshold selection to the noise reduction effect, the adaptive threshold and singular value decomposition (SVD) techniques are combined with multiwavelet analysis in this paper. Through the analysis of simulation signal, experimental signal and engineering data of bearing fault, it is shown that the denoising effect of multi-wavelet is better than that of single wavelet. Moreover, it is easier to identify the early fault features of rolling bearings. (3) in order to make full use of the information of each sub-band after multi-wavelet decomposition, a fault diagnosis method based on multi-wavelet packet is studied in this paper. With Shannon entropy as the cost function, the search and recognition of the optimal basis of multi-wavelet packets are completed. On this basis, it is combined with adaptive threshold and singular value decomposition to reduce noise. The vibration signals of bearing under different fault modes (changing the size of pitting) are used to verify the effectiveness of the noise reduction method. (4) for the normal bearing and 0.5-5mm, there are 9 kinds of analysis modes of external ring pitting. The fault degree identification factor is constructed. Among them, multi-wavelet packet decomposition and spectral kurtosis analysis are used as the main methods of feature extraction, and then combined with the complexity calculation of the feature coefficients, the pattern recognition of 9 states is completed. (5) based on the theoretical research, a fault monitoring and diagnosis system based on LabVIEW and Matlab is developed, which is based on the simulation of 6307 rolling bearing. The practicability and reliability of the system are verified.
【学位授予单位】：北京工业大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH133.33;TH165.3
【引证文献】
相关期刊论文 前3条
1 徐千;程秀芳;侯娅品;;基于小波分析的滚动轴承故障诊断研究[J];机械工程师;2012年02期
2 王红君;贺鹏;赵辉;岳有军;刘明明;;多小波对风机故障信号降噪处理的比较研究[J];化工自动化及仪表;2013年02期
3 张伟;;一种新型的旋转机械滚动轴承故障诊断方法[J];科技创新导报;2012年02期
相关硕士学位论文 前2条
1 罗琴;基于MEMS惯性传感器的微小型航姿参考系统的设计与研究[D];上海交通大学;2012年
2 郭永伟;基于支持向量机与遗传算法的故障模式识别及趋势预测方法研究[D];北京化工大学;2012年
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