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

作　　者：安学利[1] 蒋东翔[1] 刘超[1] 陈杰[1] 

机构地区：[1]电力系统及发电设备控制与仿真国家重点实验室,清华大学热能工程系,北京市100084

出　　处：《电力系统自动化》2012年第5期41-44,102,共5页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(51174273);中国博士后科学基金资助项目(20090460273)~~

摘　　要：针对风电机组调心滚子轴承故障振动信号非平稳、非线性的特点,提出了基于固有时间尺度分解(ITD)的轴承故障特征提取方法。ITD方法可以将复杂信号分解成若干个固有旋转分量和一个趋势分量之和,能准确地展示非平稳信号的动态特性,有较高的拆解效率和频率分辨率。分析结果表明,ITD方法能有效地提取风电机组轴承故障特征,可用于在线故障诊断。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 

分 类 号：TM315[电气工程—电机]