Fault Extraction of Wind Turbine Rolling Bearings Using FDEO and the Improved ACYCBD

Gong Yongli; Peng Dikang; Feng Tao; Liu Yibing

doi:10.16578/j.issn.1004.2539.2023.01.023

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Fault Extraction of Wind Turbine Rolling Bearings Using FDEO and the Improved ACYCBD

Development·Application | 更新时间：2023-06-07

- Fault Extraction of Wind Turbine Rolling Bearings Using FDEO and the Improved ACYCBD
- Journal of Mechanical Transmission Vol. 47, Issue 1, Pages: 163-169(2023)
- 作者机构：
  
  1.中国绿发投资集团有限公司，北京 100020
  2.都城伟业集团有限公司，北京 100020
  3.鲁能集团有限公司，北京 100020
  4.华北电力大学电站能量传递转化与系统教育部重点实验室，北京 102206
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.01.023
  CLC：
- Published：15 January 2023，
  
  Received：12 March 2022，
  
  Revised：25 April 2022，
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宫永立,彭迪康,冯涛等.基于FDEO与改进ACYCBD的风电机组轴承故障特征提取[J].机械传动,2023,47(01):163-169.

Gong Yongli,Peng Dikang,Feng Tao,et al.Fault Extraction of Wind Turbine Rolling Bearings Using FDEO and the Improved ACYCBD[J].Journal of Mechanical Transmission,2023,47(01):163-169.
宫永立,彭迪康,冯涛等.基于FDEO与改进ACYCBD的风电机组轴承故障特征提取[J].机械传动,2023,47(01):163-169. DOI： 10.16578/j.issn.1004.2539.2023.01.023.

Gong Yongli,Peng Dikang,Feng Tao,et al.Fault Extraction of Wind Turbine Rolling Bearings Using FDEO and the Improved ACYCBD[J].Journal of Mechanical Transmission,2023,47(01):163-169. DOI： 10.16578/j.issn.1004.2539.2023.01.023.

摘要

针对由于实际工况中风电机组轴承发生故障所采得的信号会受到变速变载的影响，造成故障特征难以提取的问题，提出了基于频域能量算子（Frequency domain energy operator，FDEO）与自适应最大2阶循环平稳盲解卷积（Adaptive maximum second order cyclostationarity blind deconvolution，ACYCBD）的风电机组轴承故障特征提取方法。首先，通过SCADA数据提供的高速轴转速平均速度对CMS（Condition monitoring system）系统采集的振动信号进行感兴趣的振动成分选择，并通过窄带滤波和FDEO对振动信号进行瞬时频率估计和阶次跟踪；其次，针对风电机组振源多、振动信号复杂的特点，对通过阶次跟踪后的角度域振动信号应用改进ACYCBD完成故障特征提取。工程应用分析结果表明，该方法能够准确有效地实现风电机组轴承特征的提取而不受到其他振源的影响。

Abstract

Rolling bearings normally work under variable speed and load conditions

and their fault becomes hard to be extracted. In this study

for a method of extracting rolling bearing fault is proposed based on the frequency domain energy operator (FDEO) and an improved adaptive maximum second order cyclostationarity blind deconvolution (ACYCBD). Firstly

high speed shaft means the frequency provided by the SCADA system is used to find the optimal vibration component for further analysis. The instantaneous frequency of the vibration component is then estimated using FDEO for order tracking. Finally

for the purpose of addressing the problem such as multiple vibration components existing in a vibration signal collected from wind turbines

which may mask the vibration component of interest

the improved ACYCBD is then used to extract the fault feature. The industrial results show that the proposed method is able to extract the faulty feature at an early stage without the interference of other vibration sources.

关键词

风电机组轴承故障频域能量算子自适应最大2阶循环平稳盲解卷积

Keywords

Wind turbineRolling bearing faultFrequency domain energy operatorAdaptive maximum second order cyclostationarity blind deconvolution

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