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Bearing Failures Detection in Induction Motors Using the Stator Current Analysis Based on Hilbert Huang Transform

机译:基于希尔伯特·黄(Hilbert Huang)变换的基于定子电流分析的感应电动机轴承故障检测

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摘要

Fault detection is a major challenge for the maintenance of asynchronous motors. The bearings defects are the most important defects that can occur in these. In this context, we propose a new approach for the detection of these defects based on the analysis of the stator current. The Ensemble Empirical Mode Decomposition is applied to the stator current experimental data of the asynchronous motor subjected to various loads in healthy and failing case. The results of the analysis of the envelope of selected Intrinsic Mode Functions, obtained by Ensemble Empirical Mode Decomposition algorithm allowed us to get a better discrimination of different bearing defects.
机译:故障检测是异步电动机维护的主要挑战。轴承缺陷是其中最重要的缺陷。在这种情况下,我们提出了一种基于定子电流分析来检测这些缺陷的新方法。在健康和故障情况下,将“集成经验模式分解”应用于异步电动机在各种负载下的定子电流实验数据。通过集成的经验模式分解算法获得的选定固有模式函数的包络分析结果,使我们能够更好地区分不同的轴承缺陷。

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