Fault Diagnosis of Rolling Bearing of Wind Turbine Based on VMD and Different Envelope Order Structure

Jianguo WANG; Yutong LIN; Ye TIAN; Peng DU; Peiyan ZHANG; Hongwei XIN; Yingjie WU

doi:10.12096/j.2096-4528.pgt.2018.011

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Fault Diagnosis of Rolling Bearing of Wind Turbine Based on VMD and Different Envelope Order Structure

NEW and Renewable Energy | 更新时间：2025-07-04

- Fault Diagnosis of Rolling Bearing of Wind Turbine Based on VMD and Different Envelope Order Structure
- Power Generation Technology Vol. 39, Issue 1, Pages: 63-69(2018)
- 作者机构：
  
  1.东北电力大学自动化工程学院, 吉林省吉林市 132012
  2.吉林中电投新能源有限公司, 吉林省长春市 130012
  3.长岭中电投第二风力发电有限公司, 吉林省松原市 131500
  4.国网襄阳市襄州区供电公司, 湖北省襄阳市 441100
- 作者简介：
- 基金信息：
  
  Science & Technology Development Plan of Jilin Province(20150203001SF)
- DOI：10.12096/j.2096-4528.pgt.2018.011
  CLC：
- Received：11 December 2017，
  
  Published：28 February 2018
- 稿件说明：
移动端阅览
Jianguo WANG, Yutong LIN, Ye TIAN, et al. Fault Diagnosis of Rolling Bearing of Wind Turbine Based on VMD and Different Envelope Order Structure[J]. Power Generation Technology, 2018, 39(1): 63-69.
DOI：

Jianguo WANG, Yutong LIN, Ye TIAN, et al. Fault Diagnosis of Rolling Bearing of Wind Turbine Based on VMD and Different Envelope Order Structure[J]. Power Generation Technology, 2018, 39(1): 63-69. DOI： 10.12096/j.2096-4528.pgt.2018.011.

摘要

针对风电机组变工况滚动轴承故障诊断问题，将变分模态分解（variational mode decomposition，VMD）与计算阶比跟踪（computed order tracking，COT）、逆包络阶次谱（reversed sequence squared envelope spectrum，RE-SES）2种方法结合进行包络阶次分析；将非平稳的时间域信号转换成平稳的角度域信号，再经包络法找出滚动轴承故障特征信息。仿真与实验结果表明，VMD结合RE-SES包络阶次法可以更加有效地提取出调幅信号中的调制阶次，并且VMD滤波可以使得故障特征阶次更加凸显，易于故障识别，为风电机组变速工况下的滚动轴承故障诊断提供参考。

Abstract

In order to identify the faults of rolling bearing of wind turbines

the variational modal decomposition (VMD) was combined with two methods

including calculation order tracking (COT) and reversed sequence squared envelope spectrum (RE-SES)

to carry out the envelope order analysis. The non-stationary signals in time domain were converted into stationary signals in angle domain by equal angle sampling. And the bearing fault characteristic information was obtained by envelope method. The results show that the modulation order of amplitude modulation signal can be extracted more effectively by VMD combined with RE-SES envelope order method. The VMD filter could make the fault feature order more prominent

and it is easy to identify the fault

and it provide reference for the fault diagnosis of rolling bearing of wind turbine under variable speed condition.

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references

左翔, 朱留存, 朱悦灏.阶比分析在旋转机械运行状态监测中的应用[J].中国设备工程, 2017(13):85-86.

康海英, 栾军英, 郑海起, 等.基于阶次跟踪和经验模态分解的滚动轴承包络解调分析[J].机械工程学报, 2007, 43(8):119-122.

Borghesanin P, Ricci R, Chatterton S, et al.A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions[J].Mechanic Systems and Signal Processing, 2013, 38(1):23-35.

姜战伟, 郑近德, 潘海洋, 等.POVMD与包络阶次谱的变工况滚动轴承故障诊断[J].振动、测试与诊断, 2017, 37(3):609-616.

武英杰. 基于变分模态分解的风电机组传动系统故障诊断研究[D]. 北京: 华北电力大学, 2016. http://cdmd.cnki.com.cn/Article/CDMD-11412-1016268858.htm .

刘科伟. 转机械阶比分析研究与软件实现[D]. 南京: 南京航空航天大学, 2008. http://cdmd.cnki.com.cn/Article/CDMD-10287-2010079966.htm .

Dragomiretskiy K, Zosso D.Variational mode decomposition[J].IEEE Transactions on Signal Processing, 2014, 62(3):531-544.

郝高岩, 刘永强, 廖英英.一种基于改进阶次包络谱的滚动轴承故障诊断算法[J].振动与冲击, 2016, 25(15):144-148.

孔德同, 贾思远, 王天品, 等.基于振动分析的风力发电机故障诊断方法[J].发电与空调, 2017, 38(1):54-58.

Abboud D, Antoni J, Sieg-Zieba S, et al.Envelope analysis of rotating machine vibrations in variable speed conditions:a comprehensive treatment[J].Mechanical Systems and Signal Processing, 2017(84):200-226.

丁康, 米林, 王志杰.解调分析在故障诊断中应用的局限性问题[J].振动工程学报, 1997, 10(1):13-20.

Muralidharan A, Sugumaran V, Soman K P, et al.Detection of gear faults in variable rotating speed using variational mode decomposition(VMD)[J]. Mechanics and Industry, 2015, 10(1):55-80.

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