基于小波包变换与粗糙集的滚动轴承故障诊断研究

发布时间：2018-03-11 20:03

本文选题：故障轴承　切入点：小波包分析　出处：《太原理工大学》2011年硕士论文　论文类型：学位论文

【摘要】：滚动轴承作为基础零部件在机械设备中起着承受载荷、传动载荷的作用,是传动系统中最关键和重要的部件,同时由于其工作环境较恶劣,也是最容易发生故障的部件,因此,分析研究轴承故障诊断技术具有重大的科学意义和现实意义。传统的轴承故障诊断方法是对滚动轴承的振动信号通过快速傅里叶变换(FFT)进行频域分析来识别故障的,严格地说这种方法只适合分析平稳的和具有高斯分布的信号,而滚动轴承的故障信号表现为非平稳性,且故障特征较微弱,因此诊断起来往往比较困难。小波包变换是一种具有较强的弱信号分辨能力的时频分析技术,与传统的FFT频域分析技术相比具有多分辨分析的特点,能够更有效地提取信号的故障特征。小波包变换在信号低频部分具有较高的频率分辨率和较低的时间分辨率,在信号的高频部分具有较高的时间分辨率和较低的频率分辨率,被称为信号分析的“显微镜”。利用小波包分解技术对动态系统的故障进行检测与诊断可以取得良好的效果。但是小波包变换也存在很多不足之处,例如在利用小波包变换进行故障诊断时需要具备一定的先验知识,如轴承的结构参数、机器转速和计算特征频率的理论公式等,而机器在运行过程中转速总会存在波动甚至是较大的变化,与转速相关的特征频率也会产生一定的波动甚至是较大范围的跳跃；其次,对于很多类型的滚动轴承,由于计算它们特征频率的理论公式很难导出,因此在对轴承进行诊断时就存在很大的不确定性。粗糙集(rough set theory)是一种新的处理模糊、不完整和不确定信息的数学工具,它不需要任何先验知识和系统的数学模型,仅从数据本身就能提取有用信息,揭示数据间的潜在规则,同时实现信息简化,非常适合机械的故障诊断。本文将小波包变换和粗糙集两者的优点结合起来,提出了一种基于小波包变换和粗糙集相结合的诊断规则获取方法,这种方法最突出的特点是不需要计算滚动轴承的故障特征频率,只需提取信号的能量特征值。最后将这种方法用于对滚动轴承的7种故障进行诊断,取得了良好的效果。
[Abstract]:Rolling bearing, as the basic part, plays the role of bearing load and driving load in mechanical equipment. It is the most critical and important part of transmission system. At the same time, because of its bad working environment, it is also the most prone to failure, so, It is of great scientific and practical significance to analyze and study bearing fault diagnosis technology. The traditional method of bearing fault diagnosis is to analyze the vibration signal of rolling bearing in frequency domain by fast Fourier transform (FFT). Strictly speaking, this method is only suitable for the analysis of stable signals with Gao Si distribution. However, the fault signal of rolling bearing is non-stationary and the fault characteristic is weak, so it is difficult to diagnose. Wavelet packet transform is a time-frequency analysis technique with strong weak signal resolution. Compared with the traditional FFT frequency domain analysis technique, it has the characteristics of multi-resolution analysis and can extract the fault feature of the signal more effectively. The wavelet packet transform has higher frequency resolution and lower time resolution in the low frequency part of the signal. In the high frequency part of the signal, it has higher time resolution and lower frequency resolution, so it is called "microscope" of signal analysis. Using wavelet packet decomposition technology to detect and diagnose the fault of dynamic system can obtain good results. However, wavelet packet transform also has many shortcomings, such as prior knowledge in fault diagnosis using wavelet packet transform, such as structural parameters of bearings, theoretical formulas for calculating machine speed and calculating characteristic frequency, and so on. And the speed of the machine always fluctuates or even changes greatly during the running process, and the characteristic frequency associated with the speed will also produce certain fluctuation and even a large range of jumps. Secondly, for many types of rolling bearings, Since the theoretical formulas for calculating their characteristic frequencies are difficult to derive, there is a great deal of uncertainty in the diagnosis of bearings. Rough set set the rough set is a new mathematical tool for dealing with fuzzy, incomplete and uncertain information. It does not require any prior knowledge or mathematical model of the system. It can extract useful information from the data itself and reveal the potential rules between the data. At the same time, the information is simplified, which is very suitable for mechanical fault diagnosis. In this paper, the advantages of wavelet packet transform and rough set are combined, and a diagnosis rule acquisition method based on wavelet packet transform and rough set is proposed. The most outstanding feature of this method is that it does not need to calculate the fault characteristic frequency of the rolling bearing, but only needs to extract the energy eigenvalue of the signal. Finally, the method is applied to diagnose 7 kinds of faults of the rolling bearing, and good results are obtained.
【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：O159;TH165.3

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