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Time-frequency vibration analysis for the detection of motor damages caused by bearing currents

机译：时频振动分析，用于检测轴承电流引起的电机损坏

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摘要

Motor failure due to bearing currents is an issue that has drawn an increasing industrial interest over recent years. Bearing currents usually appear in motors operated by variable frequency drives (VFD); these drives may lead to common voltage modes which cause currents induced in the motor shaft that are discharged through the bearings. The presence of these currents may lead to the motor bearing failure only few months after system startup. Vibration monitoring is one of the most common ways for detecting bearing damages caused by circulating currents; the evaluation of the amplitudes of well-known characteristic components in the vibration Fourier spectrum that are associated with race, ball or cage defects enables to evaluate the bearing condition and, hence, to identify an eventual damage due to bearing currents. However, the inherent constraints of the Fourier transform may complicate the detection of the progressive bearing degradation; for instance, in some cases, other frequency components may mask or be confused with bearing defect-related while, in other cases, the analysis may not be suitable due to the eventual non-stationary nature of the captured vibration signals. Moreover, the fact that this analysis implies to lose the time-dimension limits the amount of information obtained from this technique. This work proposes the use of time-frequency (T-F) transforms to analyse vibration data in motors affected by bearing currents. The experimental results obtained in real machines show that the vibration analysis via T-F tools may provide significant advantages for the detection of bearing current damages; among other, these techniques enable to visualise the progressive degradation of the bearing while providing an effective discrimination versus other components that are not related with the fault. Moreover, their application is valid regardless of the operation regime of the machine. Both factors confirm the robustness and reliability of these tools that may be an interesting alternative for detecting this type of failure in induction motors.
机译：近年来，由于轴承电流引起的电动机故障是引起越来越多的工业兴趣的问题。轴承电流通常出现在由变频驱动器（VFD）运转的电动机中。这些驱动可能会导致共同的电压模式，从而导致电机轴中感应的电流通过轴承排出。这些电流的存在可能会在系统启动后仅几个月就导致电动机轴承故障。振动监测是检测由循环电流引起的轴承损坏的最常见方法之一。通过评估与轴承座，球或保持架缺陷相关的振动傅立叶频谱中众所周知的特征分量的振幅，可以评估轴承状况，并因此确定由于轴承电流而导致的最终损坏。但是，傅立叶变换的固有约束可能会使渐进方位退化的检测变得复杂。例如，在某些情况下，其他频率分量可能会掩盖轴承相关的轴承缺陷或与轴承缺陷相关联，而在其他情况下，由于捕获的振动信号的最终非平稳性质，分析可能不适合。而且，这种分析意味着失去时维的事实限制了从该技术获得的信息量。这项工作提出了使用时频（T-F）变换来分析受轴承电流影响的电动机中的振动数据。在真实机器中获得的实验结果表明，通过T-F工具进行的振动分析可以为检测轴承电流损坏提供显着的优势。其中，这些技术可以可视化轴承的渐进退化，同时提供与其他与故障无关的组件的有效区分。而且，无论机器的运行方式如何，它们的应用都是有效的。这两个因素都证实了这些工具的坚固性和可靠性，这可能是检测感应电动机中此类故障的有趣替代方法。

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