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Quantification of Rolling- Element Bearing Fault Severity of Induction Machines

机译:感应电机滚动轴承故障严重程度的量化

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The characteristic frequencies of different types of bearing faults can be calculated by a well-defined frequency-based model that depends on the motor speed, the bearing geometry and the specific location of a defect inside a bearing. Therefore, the existence of a bearing fault as well as its specific fault type can be readily determined by performing frequency spectral analyses on the monitored signals. However, this traditional approach, despite being simple and intuitive, is not able to identify the severity of a bearing fault in a quantitatively manner. Moreover, it is often tedious and time-consuming to apply this approach to electric machines with different power ratings, as the bearing fault threshold values need to be manually calibrated for each motor running at every possible speed and carrying any possible load. This paper thus proposes a quantitative approach to estimate a bearing fault severity based on the air gap displacement profile, which is reconstructed from the mutual inductance variation profile estimated from a novel electrical model that only takes the stator current as input. In addition, the accuracy of the electrical model and the estimated bearing fault severity are validated by simulation results. The proposed method can be used to monitor bearing faults in induction machines with any power ratings that operate under any speeds and loads, and a bearing fault alarm will be triggered if the fault severity exceeds a universal threshold value.
机译:可以通过定义明确的基于频率的模型来计算不同类型轴承故障的特征频率,该模型取决于电动机速度,轴承几何形状以及轴承内部缺陷的特定位置。因此,轴承故障及其特定故障类型的存在可以通过对监控信号进行频谱分析而容易地确定。但是,这种传统方法尽管简单直观,却无法定量地确定轴承故障的严重性。此外,将这种方法应用于具有不同额定功率的电机通常是繁琐且耗时的,因为需要针对以每种可能的速度运行并承受任何可能的负载的每个电动机手动校准轴承故障阈值。因此,本文提出了一种基于气隙位移曲线估计轴承故障严重性的定量方法,该方法是从仅基于定子电流作为输入的新型电气模型估计的互感变化曲线中重建的。此外,仿真结果验证了电气模型的准确性和估计的轴承故障严重性。所提出的方法可用于监视在任何速度和负载下运行的任何额定功率的感应电机中的轴承故障,并且如果故障严重性超过通用阈值,则将触发轴承故障警报。

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