Fault Diagnosis of Wind Turbine Gearbox based on LSGAN and VMD-MPE-KELM

Cihai Qin; Ruizhi Zhao; Yueqiang Wang; Yunfei Ding; Dong Huang; Xiaolin Lian

doi:10.16578/j.issn.1004.2539.2021.11.024

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Fault Diagnosis of Wind Turbine Gearbox based on LSGAN and VMD-MPE-KELM

Development·Application | 更新时间：2022-11-10

- Fault Diagnosis of Wind Turbine Gearbox based on LSGAN and VMD-MPE-KELM
- Journal of Mechanical Transmission Vol. 45, Issue 11, Pages: 153-160(2021)
- 作者机构：
  
  1.国网上海市电力公司长兴供电公司，上海 201913
  2.上海电机学院电气学院，上海 201306
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.11.024
  CLC：
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秦辞海,赵睿智,王月强等.基于LSGAN和VMD-MPE-KELM的风机齿轮箱故障诊断[J].机械传动,2021,45(11):153-160.

Qin Cihai,Zhao Ruizhi,Wang Yueqiang,et al.Fault Diagnosis of Wind Turbine Gearbox based on LSGAN and VMD-MPE-KELM[J].Journal of Mechanical Transmission,2021,45(11):153-160.
秦辞海,赵睿智,王月强等.基于LSGAN和VMD-MPE-KELM的风机齿轮箱故障诊断[J].机械传动,2021,45(11):153-160. DOI： 10.16578/j.issn.1004.2539.2021.11.024.

Qin Cihai,Zhao Ruizhi,Wang Yueqiang,et al.Fault Diagnosis of Wind Turbine Gearbox based on LSGAN and VMD-MPE-KELM[J].Journal of Mechanical Transmission,2021,45(11):153-160. DOI： 10.16578/j.issn.1004.2539.2021.11.024.

摘要

在实际工况中，风机齿轮箱的故障样本多呈现不均衡特征。为克服样本不均衡性给诊断效果带来的影响，提出了一种基于LSGAN（最小二乘对抗网络）和VMD-MPE-KELM的风机齿轮箱故障诊断方法。首先，采用LSGAN算法用于少数类故障样本的生成处理，将具备原始样本特征的生成数据扩充样本集使其分布均衡，采用VMD方法分解样本集中各类故障的振动信号，计算各模态分量的MPE多尺度排列熵值以提取信号特征；再通过KPCA方法降维处理，获得故障样本的特征向量，将其代入KELM模型诊断。实验表明，LSGAN算法克服了GAN在生成故障样本中梯度消失、训练不稳定和数据质量差等问题；VMD-MPE-KPCA方法可有效提取故障特征。该方法有效地提高了非平衡齿轮箱故障样本的诊断精度。

Abstract

In the actual working condition，the fault samples of wind turbine gearbox are mostly unbalanced. In order to overcome the influence of sample imbalance on the diagnosis effect，a fault diagnosis method of wind turbine gearbox based on LSGAN and VMD-MPE-KELM is proposed. Firstly，LSGAN algorithm is used to generate and process a few kinds of fault samples. The generated data with original sample characteristics is expanded to make its distribution balanced. The VMD method is used to decompose the vibration signals of all kinds of faults in the sample set，and the MPE value of each modal component is calculated to extract the signal features. Then，KPCA method is used to reduce the dimension to obtain the feature vector of fault samples，which is substituted into KELM model for diagnosis. The experimental results show that LSGAN algorithm overcomes the problems of GAN gradient disappearance，unstable training and poor data quality in generating fault samples. The VMD-MPE-KPCA method can effectively extract fault features. This method effectively improves the diagnosis accuracy of unbalanced gearbox fault samples.

关键词

变分模态分解最小二乘对抗网络齿轮箱多尺度排列熵故障诊断

Keywords

Variational mode decompositionLSGANGearboxMPEFault diagnosis

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