基于VMD的滚动轴承故障诊断方法研究

发布时间：2018-05-01 03:38

本文选题：滚动轴承 + 故障诊断　；参考：《石家庄铁道大学》2016年硕士论文

【摘要】：滚动轴承是应用在旋转机械中最为广泛的零部件之一,故监测滚动轴承的工作状态,避免发生严重事故,具有重要的经济价值和现实意义。轴承故障的诊断可有效预防突发性事故的发生,是机械系统安全运行的重要保证,因此,本文以滚动轴承为研究对象,从轴承振动信号的处理着手,针对滚动轴承特征频率提取的问题,进行了一系列的研究工作。主要内容如下:首先,在理论分析和工程应用的基础上,系统阐述了此课题的研究背景及实际意义,对滚动轴承的结构、振动机理、诊断方法及各方法在滚动轴承故障特征提取中应用的研究现状、研究进展与发展趋势进行了较为全面的阐述。之后根据机械故障诊断理论阐述轴承各部分故障的特征频率的计算方法。其次,为了克服经验模态分解(empirical mode decomposition,EMD)方法中模态混叠和欠包络等缺陷,文章从频率分离、过分割、初始化收敛和非平稳信号的多模态等方面介绍了变分模态分解(variational mode decomposition,VMD)方法,通过仿真信号分析了VMD方法对非平稳信号的分解效果,实验证明,对于非平稳信号,VMD算法能很好的完成频率分离,克服模态混叠。再次,针对滚动轴承早期故障振动信号信噪比低、故障特征提取困难的问题,文章提出了基于变分模态分解和Teager能量算子的滚动轴承故障提取方法。首先对故障信号进行VMD分解,得到若干本征模态(intrinsic mode function,IMF)分量;其次,通过峭度准则选取前两个峭度最大的分量进行重构,对重构信号进行Teager能量算子解调,得到信号的Teager能量谱。将该方法应用到滚动轴承仿真故障数据和实际数据中,结果表明,该方法提高了信号的分解效率,降低了噪声的影响,能够实现滚动轴承故障的精确诊断,证明了该方法的有效性。最后,针对滚动轴承故障振动信号的非平稳特征和难以获得大量典型故障样本的实际情况,提出了基于变分模态分解排列熵和支持向量机(support vector machine,SVM)的滚动轴承故障诊断方法。该方法首先对故障信号进行VMD分解,得到若干IMF分量;求解包含有主要故障信息的IMF分量的排列熵,取各分量的排列熵组成特征向量,最后将其作为SVM的输入来实现滚动轴承的故障分类。将该方法应用到滚动轴承振动信号故障诊断中,结果表明,该方法能够实现滚动轴承故障的精确诊断,证明了该方法的有效性和可行性。
[Abstract]:Rolling bearing is one of the most widely used parts in rotating machinery, so it is of great economic value and practical significance to monitor the working condition of rolling bearing and avoid serious accidents. The diagnosis of bearing fault can effectively prevent the occurrence of sudden accident and is an important guarantee for the safe operation of mechanical system. Therefore, this paper takes the rolling bearing as the research object and starts with the processing of bearing vibration signal. Aiming at the problem of feature frequency extraction of rolling bearing, a series of research work was carried out. The main contents are as follows: firstly, on the basis of theoretical analysis and engineering application, the research background and practical significance of this subject, the structure and vibration mechanism of rolling bearings are systematically expounded. The research status, research progress and development trend of fault feature extraction of rolling bearing are discussed in detail. Then, according to the theory of mechanical fault diagnosis, the calculation method of characteristic frequency of each part of bearing fault is expounded. Secondly, in order to overcome the defects of the empirical mode decomposition (EMD) method, such as modal aliasing and underenvelope, this paper introduces the variational mode decomposition (VMD) method from the aspects of frequency separation, over-segmentation, initialization convergence and multimodal of non-stationary signals. The decomposition effect of VMD method to non-stationary signal is analyzed by simulation signal. Experiments show that the algorithm can achieve frequency separation and overcome modal aliasing. Thirdly, aiming at the problem of low signal-to-noise ratio (SNR) of early fault vibration signal and difficulty of fault feature extraction, a fault extraction method based on variational mode decomposition (VMD) and Teager energy operator is proposed in this paper. Firstly, the fault signal is decomposed by VMD, and some intrinsic mode intrinsics mode function IMF components are obtained. Secondly, the first two largest kurtosis components are selected to reconstruct the reconstructed signal, and the Teager energy operator is used to demodulate the reconstructed signal. The Teager energy spectrum of the signal is obtained. The method is applied to the rolling bearing simulation fault data and the actual data. The results show that the method improves the signal decomposition efficiency, reduces the influence of noise, and can realize the accurate diagnosis of rolling bearing fault. The effectiveness of the method is proved. Finally, a fault diagnosis method for rolling bearing based on variational mode decomposition permutation entropy and support vector machine (SVM) is presented in view of the non-stationary characteristics of rolling bearing fault vibration signal and the fact that it is difficult to obtain a large number of typical fault samples. The method firstly decomposes the fault signal by VMD, obtains some IMF components, solves the permutation entropy of the IMF component containing the main fault information, and takes the permutation entropy of each component to form the eigenvector. Finally, it is used as the input of SVM to realize the fault classification of rolling bearing. The method is applied to the fault diagnosis of rolling bearing vibration signal. The results show that the method can accurately diagnose the rolling bearing fault, and proves the effectiveness and feasibility of the method.
【学位授予单位】：石家庄铁道大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TH133.33

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