Research on Unsupervised Fault Diagnosis Method for Wind Turbine Gearbox

ZU Haidong; JIAO Xiaofeng; ZHANG Wanfu; SUN Kang; LI Chun

doi:10.19805/j.cnki.jcspe.2025.230335

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Research on Unsupervised Fault Diagnosis Method for Wind Turbine Gearbox

更新时间：2026-02-05

- Research on Unsupervised Fault Diagnosis Method for Wind Turbine Gearbox
- Chinese Journal of Power Engineering Vol. 45, Issue 1, Pages: 106-114(2025)
- 作者机构：
  
  1. 内蒙古电力科学研究院分公司,内蒙古,呼和浩特,010020
  2. 上海理工大学能源与动力工程学院,上海,200093
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.230335
  CLC： TM315
- Online First：15 January 2025，
  
  Published：2025
- 稿件说明：
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俎海东,焦晓峰,张万福,孙康,李春. 风力机齿轮箱无监督故障诊断方法研究动力工程学报, 2025, 45(1): 106-114 https://doi.

org/10.19805/j.cnki.jcspe.2025.230335
俎海东,焦晓峰,张万福,孙康,李春. 风力机齿轮箱无监督故障诊断方法研究动力工程学报, 2025, 45(1): 106-114 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230335.

org/10.19805/j.cnki.jcspe.2025.230335 DOI：

摘要

针对风力机齿轮箱振动信号具有强非线性特征

提出了改进变分模态分解方法对信号进行分解以提取特征分量

并以混沌相图及Lyapunov指数量化信号的非线性变化。采用随机近邻嵌入算法对多模态非线性故障特征集的冗余特征进行降维

以保证故障特征提取的可靠性并提升故障诊断准确率

所提出的无监督故障诊断框架无需人为对故障样本进行标注

更适合工程应用

并将所提方法应用于NREL GRC风力机齿轮箱故障。结果表明:改进变分模态分解方法可准确实现多模态特征提取

结合随机近邻嵌入算法

可有效消除冗余特征保证故障信息的可靠性

且同类样本聚集、异类样本差异增大

聚类表现更清晰

提升了故障分类的准确率。

Abstract

Aiming at strong nonlinear characteristics of wind turbine gearbox vibration signals

an improved variational mode decomposition method was proposed to decompose signals for extracting characteristic components

and the nonlinear changes of the signal were quantified by chaotic phase portraits and Lyapunov exponent. To ensure the reliability of fault feature extraction and improve the accuracy of fault diagnosis

the random nearest neighbor embedding algorithm was used to reduce redundant features of multi-modal nonlinear fault feature sets. The proposed method was applied to NREL GRC wind turbine gearbox faults due to the unsupervised fault diagnosis framework being more suitable for engineering applications without manual marking of fault samples. Results show that the improved variational mode decomposition method can accurately extract multi-modal features. Combined with the random nearest neighbor embedding algorithm

redundant features can be effectively eliminated to ensure the reliability of fault information. Moreover

the clustering of similar samples and the difference of heterogeneous samples increase

and the clustering performance is clearer

which improves the accuracy of fault classification.

关键词

Keywords

references

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