电力系统自动化2012，Vol.36Issue(5)：41-44,102,5.
基于固有时间尺度分解的风电机组轴承故障特征提取
Bearing Fault Feature Extraction of Wind Turbine Based on Intrinsic Time-scale Decomposition
摘要
Abstract
According to the non-stationary and nonlinear characteristics of the spherical roller bearing fault vibration signals in wind turbine,a bearing fault feature extraction method of wind turbine based on intrinsic time-scale decomposition（ITD） is presented.The ITD method can decompose a complex signal into several proper rotation components and a trend component.It can also reveal the dynamic characteristics of non-stationary signals,has higher decomposition efficiency and frequency resolution.The diagnosis results show that the ITD method can effectively extract the bearing fault characteristics of wind turbine and can be applied to online fault diagnosis.关键词
风电机组/调心滚子轴承/故障诊断/固有时间尺度分解/特征提取Key words
wind turbine/spherical roller bearing/fault diagnosis/intrinsic time-scale decomposition/feature extraction分类
信息技术与安全科学引用本文复制引用
安学利,蒋东翔,刘超,陈杰..基于固有时间尺度分解的风电机组轴承故障特征提取[J].电力系统自动化,2012,36(5):41-44,102,5.基金项目
国家自然科学基金资助项目 （）
中国博士后科学基金资助项目（20090460273）~~ （）
