EEMD-based notch filter for induction machine bearing faults detection
EEMD-based notch filter for induction machine bearing faults detection
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DOI：
10.1016/j.apacoust.2017.12.030
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发表时间：
2018-04-01
影响因子：
3.4
通讯作者：
Feld, G.
中科院分区：
文献类型：
--
作者：
Amirat, Y.;Benbouzid, M. E. H.;Feld, G.
提出了一种基于经验模态分解和统计工具相结合的感应电机轴承故障检测方法。特别是，它提出了一种创新的故障检测器，是基于主导的本征模式函数提取，通过合奏经验模式分解，然后取消。该方法的验证是基于仿真和实验。所取得的仿真和实验结果清楚地表明，所提出的方法是非常适合于轴承故障检测，无论故障引入的固有模式函数的秩。
This paper deals with induction machine bearing faults detection based on an empirical mode decomposition approach combined to a statistical tool. In particular, it is proposed an innovative fault detector that is based on the dominant intrinsic mode function extraction, through an ensemble empirical mode decomposition, then its cancellation. The validation of this approach is based on simulations and experiments. The achieved simulation and experimental results clearly show that the proposed approach is well suited for bearing faults detection regardless the rank of the intrinsic mode function introduced by the fault.