机械故障诊断中的多频率成分辨识方法

发布时间：2018-03-26 02:48

本文选题：状态监控与故障诊断　切入点：插值离散傅里叶变换　出处：《电子科技大学》2011年硕士论文

【摘要】：随着科学技术的发展,为了满足工业产品对可靠性与安全性的需求,通过对机械设备进行故障诊断与状态监控来避免故障及事故的发生变得越来越重要,然而随着机械设备的复杂程度不断提高,将会导致在机械设备在运行过程中发生多重故障,因此对于机械系统的多重故障特征提取也成为故障诊断领域研究的一个方向。在多重故障发生的情况下,将会发生频谱叠加、相关故障频率相近等问题。因此,关于机械故障诊断中的多频率成分辨识方法的研究,对于机械系统多重故障特征提取与多重故障诊断具有重要的理论与现实意义。本文应用基于最大旁瓣衰减窗口的插值离散傅里叶变换(IpDFT)方法进行机械系统故障特征提取与诊断的研究,同时提出了Zoom IpDFT方法进行两种或几种相近频谱的细化分析。在本文中,首先应用基于最大旁瓣衰减窗口的IpDFT方法进行轴承信号的研究,并提出了一种基于相角的频谱叠加判定方法。通过仿真信号与真实信号的分析表明,与传统的FFT方法及Zoom FFT方法相对比,基于最大旁瓣衰减窗口的IpDFT方法及Zoom IpDFT方法可以准确及稳定的进行轴承信号的故障特征提取,并且频谱叠加判定方法有效。然后本文应用Zoom IpDFT方法,通过提出描述齿轮故障程度的参数故障指数与衡量齿轮故障发生的阈值进行齿轮的早期故障的研究。与其他方法对比的结果表明,本文所应用的方法不仅能够进行早期故障的自适应诊断,同时也可以通过人工经验进行诊断,更准确的反应出齿轮的性能状态,更适应在线早期故障诊断。
[Abstract]:With the development of science and technology, in order to meet the demand of reliability and safety of industrial products, it becomes more and more important to avoid the failure and accidents by fault diagnosis and condition monitoring of machinery and equipment. However, with the increasing complexity of mechanical equipment, it will lead to multiple failures in the operation of mechanical equipment. Therefore, the multi-fault feature extraction of mechanical system has become a research direction in the field of fault diagnosis. In the case of multiple faults, the frequency spectrum superposition will occur, and the related fault frequency will be similar, and so on. Research on the identification method of multi-frequency components in mechanical fault diagnosis, This paper applies the interpolating discrete Fourier transform (IpDFT) method based on the maximum sidelobe attenuation window to the fault diagnosis of mechanical system. The study of sign extraction and diagnosis, At the same time, the Zoom IpDFT method is proposed to refine two or several similar spectrum. In this paper, the IpDFT method based on the maximum sidelobe attenuation window is applied to study the bearing signal, and a spectrum superposition method based on phase angle is proposed. Compared with the traditional FFT method and Zoom FFT method, the IpDFT method and Zoom IpDFT method based on the maximum sidelobe attenuation window can accurately and stably extract the fault feature of bearing signal. And the spectrum superposition method is effective. Then the Zoom IpDFT method is used in this paper. The early fault of gear is studied by proposing the parameter fault index describing the degree of gear fault and the threshold to measure the occurrence of gear fault. The results of comparison with other methods show that, The method used in this paper not only can be used for adaptive diagnosis of early faults, but also can be diagnosed by artificial experience, which can more accurately reflect the performance of gears and be more suitable for on-line early fault diagnosis.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH165.3

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