南京航空航天大学学报2025，Vol.57Issue(3)：509-516,8.DOI:10.16356/j.1005-2615.2025.03.012
信息融合的改进SVM风电齿轮箱故障诊断方法
Improved SVM for Fault Diagnosis of Wind Turbine Gearbox with Information Fusion
蔺思玮 1徐志科1
作者信息
- 1. 东南大学电气工程学院,南京 210096
- 折叠
摘要
Abstract
To improve the operational efficiency of wind turbines and optimize the operation and maintenance costs of wind farms,this paper combines time-domain feature index analysis with multi-sensor information fusion technology to propose a wind turbine gearbox state monitoring method based on grey wolf optimization(GWO)algorithm-support vector machine(SVM).Firstly,different time-domain statistical eigenvalues representing vibration energy are calculated,and parallel stacking is used for feature level and data level fusion to obtain an information fusion matrix.Secondly,on this basis,establish a fault diagnosis classification model based on GWO-SVM.Finally,the proposed method is validated and analyzed using the measured data of the gearbox collected from the QPZZ-Ⅱ rotating machinery vibration test.The results show that this method is significantly better than other traditional methods,and its classification and diagnostic accuracy demonstrate significant advantages.关键词
故障诊断/风电齿轮箱/灰狼优化-支持向量机/时域分析/信息融合Key words
fault diagnosis/wind turbine gearbox/grey wolf optimization-support vector machine(GWO-SVM)/time domain analysis/information fusion分类
信息技术与安全科学引用本文复制引用
蔺思玮,徐志科..信息融合的改进SVM风电齿轮箱故障诊断方法[J].南京航空航天大学学报,2025,57(3):509-516,8.
