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Analysis of Ball Bearing Defects in Synchronous Machines Using Electrical Measurements

机译：同步电机中滚珠轴承缺陷的电气测量分析

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摘要

Rolling element bearings are used in most electrical machines, especially for small and medium size applications. Under non-ideal operating conditions, ball bearing condition degrades by fatigue, ambient vibration, misalignment, overloading, contamination, corrosion from water or chemicals, improper lubrication, shaft currents and residual stress left from the bearing manufacturing process. All of these conditions eventually lead to increased vibration and acoustic noise during machine operation which at some point in time results in unexpected bearing failure. Over the years, a great number of publications have been devoted to the detection of mechanical faults, including rolling element bearing defects and torsional defects, in electrical machines based on Electrical Signature Analysis (ESA). It has been observed that these faults can affect either the stator to rotor air-gap distribution or the running speed of the machine, which can be reflected in the signature of the electrical signals. However, the physical link between the mechanical degradation and the electrical signature is still not explained well.;A multi-physics model is developed by joining the detailed mechanical model of a rotor bearing system and the electrical model of a synchronous machine in this research. This combined model is capable of describing the transmission of information originating from bearing faults and their impact on the variations of the measured electrical signals. The electrical machine model is developed based on winding function approach and its validity is demonstrated by a more accurate Finite Element Method (FEM) model. The mechanical model consists of a high fidelity rotor-bearing system with detailed nonlinear ball bearing model and a flexible finite element shaft model. It is validated using the housing vibration data collected from some experiments.;Generalized roughness bearing anomalies are linked to load torque ripples and airgap variations, while being related to current signature by phase and amplitude modulation. Considering that the induced characteristic signatures are usually subtle broadband changes in the current spectra, these signatures are easily affected by input power quality variations, machine manufacturing imperfections and environmental noise. In this research, a new algorithm is proposed to isolate the influence of the external disturbances of power quality, machine manufacturing imperfections and environmental noise, and to improve the effectiveness of applying the ESA for generalized roughness bearing defects. The results show that the proposed method is effective in analyzing the generalized roughness bearing anomaly in synchronous machines. Furthermore, the electrical signatures are analyzed in a synchronous machine with bearing defects. The proposed fault detection method employs a Zoomed Fast Fourier Transform (ZFFT) and Principal Component Analysis (PCA) and it is also tested on the available experimental data. The results show that amplitude induced electrical harmonics are related to the level of vibration, and the electrical signatures are affected heavily by other variables, such as power quality and load fluctuation. The proposed method is shown to be effective on detecting generalized roughness bearing defects in synchronous machines.
机译：滚动轴承用于大多数电机，尤其是中小型应用。在非理想的工作条件下，球轴承的状态会因疲劳，环境振动，不对中，过载，污染，水或化学物质的腐蚀，润滑不当，轴电流和轴承制造过程中产生的残余应力而降低。所有这些条件最终会导致机器运行过程中振动和声音的增加，从而在某个时间点导致轴承意外故障。多年来，许多出版物致力于基于电气特征分析（ESA）的电机中的机械故障检测，包括滚动元件轴承缺陷和扭转缺陷。已经观察到这些故障会影响定子到转子的气隙分布或电机的运行速度，这可以反映在电信号的签名中。然而，机械降级与电气特征之间的物理联系仍未得到很好的解释。；通过将转子轴承系统的详细机械模型与同步电机的电气模型结合起来，建立了一个多物理场模型。该组合模型能够描述源自轴承故障的信息的传输及其对测量的电信号变化的影响。基于绕组函数方法开发了电机模型，并通过更精确的有限元方法（FEM）模型证明了其有效性。力学模型由高保真转子轴承系统，详细的非线性滚珠轴承模型和柔性有限元轴模型组成。使用从一些实验中收集到的壳体振动数据可以对它进行验证。广义的粗糙度轴承异常与负载转矩波动和气隙变化有关，而与相位和幅度调制的电流信号有关。考虑到感应特性签名通常是电流频谱中的细微宽带变化，因此这些签名很容易受到输入功率质量变化，机器制造缺陷和环境噪声的影响。在这项研究中，提出了一种新的算法，以隔离电能质量，机械制造缺陷和环境噪声等外部干扰的影响，并提高将ESA应用于广义粗糙度轴承缺陷的有效性。结果表明，该方法对于分析同步电机中的广义粗糙度轴承异常是有效的。此外，在带有轴承缺陷的同步电机中分析电信号。所提出的故障检测方法采用缩放快速傅里叶变换（ZFFT）和主成分分析（PCA），并在可用的实验数据上进行了测试。结果表明，幅度感应电谐波与振动水平有关，电信号受其他变量（如电能质量和负载波动）的影响很大。结果表明，所提出的方法对于检测同步电机中的广义粗糙度轴承缺陷是有效的。

著录项

  • 作者

    Wang, Tengxi.;

  • 作者单位

    Texas A&M University.;

  • 授予单位 Texas A&M University.;
  • 学科 Mechanical engineering.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 127 p.
  • 总页数 127
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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