摘要
为了更好地识别出复杂条件下风力风电机组主轴承的运行状态,提出了基于固有时间尺度分解(ITD)和最小二乘支持向量机(LS-SVM)的风电机组轴承故障诊断方法。该方法首先将调心滚子轴承振动信号分解成若干个固有旋转分量和一个趋势分量之和。然后,对前几个固有旋转分量的瞬时幅值进行频谱分析,找出频谱中外圈、内圈、滚动体故障特征频率处以及转动频率处的幅值,将其作为故障特征向量。最后,将故障特征向量输入LS-SVM来识别机组轴承的运行状态。实验结果表明,该方法可以快速、较准确地诊断出风力发电机组轴承故障。
In order to better identify the complex running conditions of main shaft bearings,the fault diagnosis based on ITD （Intrinsic Time-scale Decomposition） and LS-SVM （Least Square-Support Vector Machine ） is proposed for wind turbine,which decomposes the bearing vibration signal into several proper rotation components and a trend component,analyzes the spectrum of instantaneous amplitude for the first few proper rotation components ,finds the fault feature frequencies of outer race ,inner race and roller,takes their amplitudes as the fault feature vectors,and inputs these fault feature vectors to LS-SVM to identify the operating conditions of bearings. Experimental results show that,the proposed method can quickly and more accurately diagnose the faults of wind turbine bearings.
出处
《电力自动化设备》
EI
CSCD
北大核心
2011年第9期10-13,共4页
Electric Power Automation Equipment
基金
国家重点基础研究发展计划项目(973项目)(2007-CB210304)
中国博士后科学基金资助项目(20090460273)~~
关键词
固有时间尺度分解
故障特征频率幅值
支持向量机
最小二乘支持向量机
风力发电机组
调心滚子轴承
故障诊断
intrinsic time-scale decomposition
fault feature frequency amplitude
support vector machines
least square-support vector machine
wind turbines
spherical roller bearing
fault detection
