Condition Monitoring of Wind Turbine Gearbox Based on IACrossformer-ESPOT

ZHANG Jian; LIU Shuai; LIU Weiliang; ZHANG Qiliang; LIU Changliang; WANG Xin; XU Jiahao; ZHANG Zhaoyan

doi:10.19805/j.cnki.jcspe.2026.250001

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Condition Monitoring of Wind Turbine Gearbox Based on IACrossformer-ESPOT

更新时间：2026-10-10

- Condition Monitoring of Wind Turbine Gearbox Based on IACrossformer-ESPOT
- Chinese Journal of Power Engineering Vol. 46, Issue 4, Pages: 127-137(2026)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院,北京,102206
  2. 保定市综合能源系统状态检测与优化调控重点实验室,河北,保定,071003
  3. 华北电力大学河北省发电过程仿真与优化控制技术创新中心,河北,保定,071003
  4. 国家能源集团新能源技术研究院有限公司,北京,102209
  5. 河北大学电子信息工程学院,河北,保定,071002
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250001
  CLC： TK83
- Published：2026
- 稿件说明：
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张健, 刘帅, 刘卫亮, 张启亮, 刘长良, 王昕, 徐家豪, 张照彦. 基于IACrossformer-ESPOT的风电机组齿轮箱状态监测[J]. 动力工程学报, 2026, 46(4): 127-137.
张健, 刘帅, 刘卫亮, 张启亮, 刘长良, 王昕, 徐家豪, 张照彦. 基于IACrossformer-ESPOT的风电机组齿轮箱状态监测[J]. 动力工程学报, 2026, 46(4): 127-137. DOI： 10.19805/j.cnki.jcspe.2026.250001.

DOI： 10.19805/j.cnki.jcspe.2026.250001

摘要

为实现风电机组齿轮箱故障的早期预警

针对现有模型在时域和频域特征提取方面的不足

以及传统阈值设置方法适应性差的问题

提出一种基于时频特征提取网络(IACrossformer)-流式自适应阈值法(ESPOT)的风电机组齿轮箱状态监测方法。首先

对风电机组数据采集与监控系统(SCADA)数据进行预处理

并通过最大信息系数(MIC)筛选出与齿轮箱油温相关的变量

将其作为模型输入;然后

借助交互式卷积(ICB)和自适应频谱(ASB)的特征提取能力

基于IACrossformer算法有效捕捉齿轮箱油温数据时频域中的复杂模式变化

从而建立齿轮箱正常油温模型;最后

采用ESPOT算法自适应地设置预测残差的阈值。结果表明:所提出的方法可精确建立齿轮箱正常油温模型

且阈值设置算法具有良好的参数适应性

能够实现齿轮箱故障的早期预警。

Abstract

In order to realize early warning of wind turbine gearbox faults

aiming at problems of insufficient feature extraction of existing models in time domain and frequency domain and poor adaptability of traditional threshold setting methods

a condition monitoring method of the wind turbine gearbox based on IACrossformer-ESPOT was proposed. Firstly

the SCADA data of wind turbine were preprocessed

and variables related to the oil temperature of the gearbox were selected as the model input through the maximum information coefficient (MIC). Then

with the feature extraction ability of interactive convolution (ICB) and adaptive spectrum (ASB)

the complex pattern changes in the time-frequency domain of the gearbox oil temperature data were effectively captured based on the IACrossformer algorithm

and the normal oil temperature model of the gearbox was established. Finally

the ESPOT algorithm was used to adaptively set the threshold of the prediction residual to achieve efficient residual analysis and condition monitoring. Results show that the proposed method can accurately establish the normal oil temperature model of gearbox

and the threshold setting algorithm has good parameter adaptability

which can realize the early warning of gearbox fault.

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references

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