摘要
针对风电机组齿轮箱工况复杂多变,提出了一种基于Gabor重排对数时频脊流形早期故障预警方法.该方法首先研究提取Gabor重排对数时频谱的脊线,构建早期故障高维特征向量;然后研究改进局部切空间流形学习方法,进行维数约简;最后采用K-近邻分类器,实现变工况风电机组齿轮箱的早期故障识别与预警.通过变转速、变载荷等多种工况的行星齿轮箱磨损试验与风电机组现场运行数据验证,结果表明该方法有效提高了复杂变工况风电机组齿轮箱早期故障预警准确率,可为其预知维护提供可靠依据.
Aiming at the complex working conditions of wind turbine gearbox,a new early fault warning method was proposed based on the Gabor rearrangement logarithmic time-frequency ridges manifold.Firstly,the ridges of Gabor rearrangement logarithmic time-frequency spectrum were extracted and the high dimensional early fault feature vector was built.Then,LTSA（local tangent space alignment）manifold learning method was studied and improved to achieve the reduction of high dimensional feature vector.Finally,the K-nearest neighbor classifier was applied to complete the early fault identification and warning of variable conditional wind turbine gear box.Many experiments were carried out to get verifying data from different condition,including variable speed,load working conditions of planetary gearbox and wind turbine operation filed.The results show that the proposed method can improve the early fault warning accuracy of wind turbine gearbox that works under complex non-stationary conditions,and can provide a reliable basis for predictive maintenance.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2017年第9期942-947,共6页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(51275052
51575055)
北京市自然基金重点项目(3131002)
国家"八六三"计划项目(2015AA043702)
"高档数控机床与基础制造装备"科技重大专资助项目(2015ZX04001002)
关键词
变工况
时频脊
流形学习
早期故障预警
variable condition
time-frequency ridge
manifold learning
early failure warning
