摘要
经验模态分解在滚动轴承和齿轮故障检测中的有效性已经得到验证,将其用于滑动轴承声发射信号研究,并结合峭度等统计指标,计算各本征模态函数的指标值。通过对某310 MW汽轮机组滑动轴承现场试验的实测声发射信号进行研究,发现在不同润滑状态下,原始信号的各指标值并未发生明显变化,而不同本征模态函数的各指标值对状态变化更为敏感,通过对比找到润滑状态变化时产生规律性变化的2个本征模态函数的统计指标,为监测滑动轴承的润滑状态提供一定的依据。
The effectiveness of Empirical Mode Decomposition （EMD）is verified in fault detection for rolling bearings and gears.EMD is applied to study acoustic emission signals of sliding bearing.The indicator values for each intrinsic mode function are calculated combining statistical parameters such as kurtosis.Through study on measured acoustic e-mission signals of sliding bearing obtained during field testing of a 310MW turbine -generator set,it is found that each indicator of original signals is not significantly changed under various lubrication states.Each indicator of different in-trinsic mode function is more sensitive to change of state.The statistical indicator of two intrinsic mode functions is found through comparison,which varies regularly with variation of lubrication state .A certain basis is provided for mo-nitoring of lubricating state of sliding bearing.
出处
《轴承》
北大核心
2015年第3期54-58,共5页
Bearing
基金
湖南省研究生科研创新项目(CX2014B382)
关键词
滑动轴承
声发射
经验模态分解
状态检测
统计指标
sliding bearing
acoustic emission
empirical mode decomposition
state detection
statistical indicator
