A fault diagnosis method of wind turbine bearings based on an enhanced morphological filter

被引：0
作者：
Qi Y. [1 ,2 ]
Fan J. [1 ,2 ]
Li Y. [1 ,2 ]
Gao X. [3 ]
Liu L. [1 ,2 ]
机构：
[1] Institute of Electric Power, Inner Mongolia University of Technology, Huhhot
[2] Laboratory of Electrical and Mechanical Control, Hohhot
[3] Faculty of Information, Beijing University of Technology, Beijing
来源：
| 1600年 / Chinese Vibration Engineering Society卷 / 40期
关键词：
Energy operator; Fault diagnosis; Feature extraction; Mathematical morphology; Vibration signal;
D O I：
10.13465/j.cnki.jvs.2021.04.029
中图分类号：
学科分类号：
摘要：
The weak fault-related features of vibration signal, which originates from wind turbine rolling element bearings, are generally immersed in environmental noise and harmonic interference and difficult to extract. This issue is addressed in this paper by proposing a new enhanced morphological filtering scheme for fault diagnosis. Firstly, a new morphology analysis method, named morphological comprehensive filter-hat transform (MCFHT), was constructed to extract fault-related impulses from measured signal in strong background noise. And its filtering property was investigated by the nonlinear filter frequency response characteristics, which provides a theoretical basis for the application of fault-related impulses extraction. Secondly, an adaptive scale selection strategy was explored to obtain appropriate filter scale for MCFHT. Thirdly, an improved envelope derivative energy operator was utilized to enhance the impulse characteristics of the signal after morphological filtering and to suppress the frequency of in-band noise. In the both simulation and experimental studies for wind turbine bearing, the proposed method delivered better fault feature extraction and noise reduction performance than the traditional methods. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：212 / 220
页数：8
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