Fault diagnosis of large-scale wind turbine bearing based on enhanced combination gradient morphological filter

被引：0
|
作者：
Zhao S. [1 ]
Chen C. [1 ,2 ]
Luo Y. [1 ]
Miao B. [1 ]
机构：
[1] School of Mechanical Engineering, Shenyang University of Technology, Shenyang
[2] Liaoning Vibration and Noise Control Engineering Research Center, Shenyang
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 07期
关键词：
Bearing; Fault diagnosis; Mathematical morphology; Signal processing; Wind turbine;
D O I：
10.19912/j.0254-0096.tynxb.2019-1266
中图分类号：
学科分类号：
摘要：
In view of the harsh working environment of large-scale wind turbines, the fault feature information of their generator bearings is difficult to detect due to strong background noise and other interference. In this paper, a new enhanced combination gradient morphological filter (ECGMF) is proposed to detect the fault feature information of rolling bearings. Combined with the characteristics of improved basic morphological operators, the proposed method can not only preserve the fault characteristics of the signal, but also suppress the noise interference. To overcome the scale instability in selecting structure elements based on kurtosis criteria and signal-to-noise ratio, the characteristic frequency intensity coefficient (CCFI) is adopted in this paper to select the optimal structure element. Simulation and experimental results show that the proposed method can effectively extract the fault feature information of wind turbine bearing. Compared with other morphological filters, the results show that the proposed method is effective and advantageous. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：424 / 430
页数：6
相关论文
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