多分支时间序列预测与迁移学习相结合的齿轮箱状态监测

赵文清; 林炜超

doi:10.19805/j.cnki.jcspe.2025.240394

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多分支时间序列预测与迁移学习相结合的齿轮箱状态监测

数字化与智能化 | 更新时间：2026-10-10

- 多分支时间序列预测与迁移学习相结合的齿轮箱状态监测
- Monitoring for Gearbox Condition Based on Multi-branch Time Series Prediction and Transfer Learning
- 动力工程学报 2025年45卷第8期页码：1319-1329
- 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. 复杂能源系统智能计算教育部工程研究中心,河北,保定,071003
- 作者简介：
  
  赵文清(1973—),女,山西朔州人,教授,博士,研究方向为风电机组健康状态监测,E-mail:zhaowenqing@ncepu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62371188);河北省自然科学基金资助项目(F2021502013)
- DOI：10.19805/j.cnki.jcspe.2025.240394
  中图分类号：
- 网络首发：2025-08-12，
  
  纸质出版：2025
- 稿件说明：
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赵文清,林炜超. 多分支时间序列预测与迁移学习相结合的齿轮箱状态监测动力工程学报, 2025, 45(8): 1319-1329 https://doi.

org/10.19805/j.cnki.jcspe.2025.240394
赵文清,林炜超. 多分支时间序列预测与迁移学习相结合的齿轮箱状态监测动力工程学报, 2025, 45(8): 1319-1329 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240394.

org/10.19805/j.cnki.jcspe.2025.240394 DOI：

摘要

为了提高利用监控和数据采集(supervisory control and data acquisition

SCADA)多变量长时间序列预测齿轮箱油温的精度

解决不同风电机组因处不同运行环境导致的数据分布不一致的问题

提出了一种基于多分支时间序列预测与迁移学习相结合的齿轮箱状态监测方法。首先

利用极致梯度提升(extreme gradient boosting

XGBoost)算法筛选输入参数组成原始序列

对其进行分解得到季节与趋势序列。其次

提出季节、趋势序列特征提取模块获取季节及趋势特征的序列

将其与经过Informer模型处理后的特征序列进行融合后输入进多层感知机映射成最终的预测值

以构建提出的多分支时间序列预测网络(multi-branch time series prediction network

MBFN)。最后

利用迁移学习并结合一分类向量支持机(one-class support vector machine

OCSVM)模型及滑动窗口构建齿轮箱的健康指数

完成齿轮箱状态监测。实验结果表明

所提出模型的MBFN显著提高了油温预测精度

优于常规时间序列预测模型

所使用的迁移策略能以较少数据适应不同数据的分布

进而实现对齿轮箱的状态监测

并且所提出的模型可以提前18.9 d发出齿轮箱故障预警。

Abstract

In order to improve the accuracy of predicting gearbox oil temperature by using supervisory control and data acquisition (SCADA) with multivariate long time series

and solve the problem of inconsistent data distribution caused by different wind power generation units under different operating environments

a method for gearbox condition monitoring was proposed based on multi-branch time series prediction and transfer learning. Firstly

the original sequence was formed by filtering input parameters with extreme gradient boosting (XGBoost) algorithm

so as to obtain the seasonal and trend sequence through decomposing. Secondly

the feature extraction module of seasonal and trend sequence was proposed to obtain the feature of seasonal and trend sequence

which was fused with the feature sequence processed by Informer model and then input into the multi-layer perceptron to map into the final prediction value

so as to construct the proposed multi-branch time series prediction network (MBFN). Finally

the health index of gearbox was constructed by using transfer learning combined with one-class support vector machine (OCSVM) model and sliding window

so as to complete the condition monitoring of gearbox. Experimental results show that the MBFN from proposed model has significantly improved the prediction accuracy of oil temperature

which is better than the conventional time series prediction model. Meanwhile

the adopted migration strategy can adapt to the distribution of different data with less data

so as to realize the condition monitoring of gearbox

while the proposed model can issue the gearbox fault warning with 18.9 days in advance.

关键词

Keywords

references

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