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A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

机译:一种改进的EEMD分解,用于检测滚动轴承故障

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The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.
机译:滚动元件轴承的振动信号是非线性的,并且难以提取相应的故障特征。 EEMD(集成经验模式分解)可有效检测轴承故障。在本调查中,提出了Meemd(改进的EEMD)以诊断轴承的外部和内部血管故障。使用Memd分解和Hilbert频谱中提取的IMFS(内在模式功能)分析了原始振动信号。 Meemd和EEMD之间比较的数值和实验结果表明,该方法更有效地提取轴承外部和内圈的故障特征而不是EEMD。

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