摘要
采用振动信号对风力发电机组滚动轴承的状态进行监测。运用经验模态分解方法对轴承振动信号进行模态分解,获得了振动信号的本征模函数。依据振动信号随轴承磨损的变化特征,采用希尔伯特-黄变换对分解后的本征模函数进行处理,得到了与本征模态函数对应的时频谱和边际谱。研究结果表明在时频谱和边际谱中呈现的特征量与轴承状态之间存在密切联系,根据振动信号的时频谱特征和边际谱特征可实现轴承故障状态的监测。
The rolling bearing state of wind turbine generator set is monitored by using the vibration signal. The empirical mode decomposition （EMD） method is used to decompose modal for vibration signal and obtain the Intrinsic Mode Function （IMF） of vibration signal. According to the change characteristics of vibration signal with bearing wear, IMF is processed by using Hilbert-Huang transformation to obtain the time-frequency spectrum and the marginal spectrum. The results show that the characteristic quantity and bearing condition in the marginal spectrum and the time-frequency spectrum are closely related, and rolling bearing condition can be monitored according to the spectrum feature and the time-frequency spectrum feature of vibration signal.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2012年第20期79-82,88,共5页
Power System Protection and Control
关键词
经验模态分解
本征模函数
希尔伯特-黄变换
滚动轴承
风力发电机
empirical mode decomposition
intrinsic mode function
Hilbert-Huang transformation
rolling bearing
wind generator
