EEMD-based notch filter for induction machine bearing faults detection

被引：50

作者：

Amirat, Y. ^{[1
]}

Benbouzid, M. E. H. ^{[2
,3
]}

Wang, T. ^{[3
]}

Bacha, K. ^{[4
]}

Feld, G. ^{[1
]}

机构：

[1] ISEN Brest, CNRS, FRE 3744, IRDL, Brest, France

[2] Univ Brest, CNRS, FRE 3744, IRDL, Brest, France

[3] Shanghai Maritime Univ, Shanghai, Peoples R China

[4] Univ Tunis, ENSIT, Tunis, Tunisia

来源：

APPLIED ACOUSTICS | 2018年 / 133卷

关键词：

Induction machine; Bearing fault; Detection; Ensemble empirical mode decomposition; Intrinsic mode function; EMPIRICAL MODE DECOMPOSITION; PARKS VECTOR APPROACH; STATOR CURRENT; CONCORDIA TRANSFORM; VIBRATION SIGNALS; FAILURE-DETECTION; HILBERT SPECTRUM; DIAGNOSIS; MOTORS; CLASSIFICATION;

D O I：

10.1016/j.apacoust.2017.12.030

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：202 / 209

页数：8

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[2]

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