基于HHT的风力发电机组滚动轴承故障特征提取  

Feature extraction of rolling bearing for wind generator based on HHT

作　　者：刘毅力[1] 陶学军 李佳 初永刚 田勇 

机构地区：[1]西安工程大学电信学院,陕西西安710048 [2]许继电气股份有限公司,河南许昌461000

出　　处：《电力系统保护与控制》2012年第20期79-82,88,共5页Power System Protection and Control

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.

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 

分 类 号：TM315[电气工程—电机]