Research on Wind Turbine Gearbox Fault Warning Method Basedon Neural Basis Expansion Analysis

WEI Le; SHU Xiaohai; CHEN Yuanye; FANG Fang

doi:10.19805/j.cnki.jcspe.2026.240732

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Research on Wind Turbine Gearbox Fault Warning Method Basedon Neural Basis Expansion Analysis

更新时间：2026-10-10

- Research on Wind Turbine Gearbox Fault Warning Method Basedon Neural Basis Expansion Analysis
- Chinese Journal of Power Engineering Vol. 46, Issue 2, Pages: 146-156(2026)
- 作者机构：
  
  华北电力大学控制与计算机工程学院,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.240732
  CLC：
- Online First：10 February 2026，
  
  Published：10 February 2026
- 稿件说明：
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魏乐,舒孝海,陈远野,房方. 基于神经基拓展分析的风电机组齿轮箱故障预警动力工程学报, 2026, 46(2): 146-156 https://doi.

org/10.19805/j.cnki.jcspe.2026.240732
魏乐,舒孝海,陈远野,房方. 基于神经基拓展分析的风电机组齿轮箱故障预警动力工程学报, 2026, 46(2): 146-156 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240732.

org/10.19805/j.cnki.jcspe.2026.240732 DOI：

摘要

为解决现有齿轮箱故障预警方法中特征冗余、预测精度低、泛化能力弱及误报率高的问题

提出了一种基于神经基拓展分析的预警方法。该方法将Sigmoid函数曲线与基于密度的空间聚类算法相结合

从原始数据中提取正常监测数据;通过改进的灰色关联度算法为不同数据点赋予权重

深度挖掘变量信息

从高维监控与数据采集系统采集的数据中提取与齿轮箱油温相关的特征变量;利用局部非线性投影将目标信号分解为基函数

实现对齿轮箱状态变量的高精度预测;通过滑动窗口和置信区间设置故障阈值

减少误报率。最后

基于实际风场数据进行验证。结果表明:在齿轮箱状态预测中基于NBEATSx模型的故障预警方法显著提升了预测精度

较传统模型降低了误报率

具备提前数小时预知故障的能力

从而有效保障了风电机组的稳定运行。

Abstract

To address issues of feature redundancy

low prediction accuracy

weak generalization ability

and high false alarm rates in existing gearbox fault warning methods

a fault warning method based on neural basis expansion analysis was proposed. The method combined Sigmoid growth curve with a density-based spatial clustering algorithm to extract normal monitoring data from raw data. An improved grey relational analysis algorithm was used to assign weights to different data points

variable information was deeply explored. Feature variables related to the oil temperature of the gearbox were extracted from the data collected by the high-dimensional monitoring and data acquisition system. Local nonlinear projection was used to decompose the target signal into basis functions to predict the state variables of the gearbox accurately

fault thresholds were set by sliding window and confidence interval to reduce the false alarm rate. The method was verified based on actual wind field data. Results show that the fault prediction method based on the NBEATSx model significantly improves the prediction accuracy in the state prediction of gearboxes

reduces the false alarm rate compared to traditional models

and has the ability to predict faults several hours in advance

thereby effectively ensuring the stable operation of wind turbine units.

关键词

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references

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