摘要
针对轴承故障成分常以周期性冲击成分出现在振动信号中,而冲击响应成分常被强大噪声淹没,造成轴承故障特征提取困难等问题,将集成经验模态分解(EEMD)与改进形态滤波方法相结合,在本征模态函数(IMF)及形态学结构元素(SE)选取时均以峭度准则为依据,对筛选出的IMF分量进行信号重构后,再进行基于峭度准则的改进形态滤波方法处理。结果表明,该方法可避免共振解调中中心频率及滤波频带选取,自适应性较好;通过对实际滚动轴承内外圈故障分析,该方法可清晰准确提取到故障特征信息,噪声抑制效果好,可用于轴承故障精确诊断。
Bearing faults are always observed as cyclical impulses in the vibration signal.In order to effectively remove the strong noise immersing the impulsive response signals and detect the cyclic impulses in the signals for bearing faults diagnoisis,a hybrid method combining the ensemble empirical mode decomposition （EEMD）method with an improved morphological filtering based on kurtosis criterion was proposed.In the method,a new decision strategy of intrinsic mode function （IMF）and morphological structure element （SE）was suggested in accordance with the kurtosis criterion.The signal reconstructed by the selected IMFs was processed by the improved morphological filtering based on kurtosis criterion.The method presented avoids the selection of center frequency and filter band in resonance demodulation method and has good adaptability.When analyzing the inner and outer ring faults of rolling bearing,the method shows its good ability of distinctly and accurately extracting the fault information and the noise is well suppressed.
出处
《振动与冲击》
EI
CSCD
北大核心
2015年第2期38-44,共7页
Journal of Vibration and Shock
基金
国家自然科学基金项目(51379080)
关键词
EEMD
形态滤波
峭度
故障诊断
轴承
EEMD
morphological filtering
kurtosis
fault diagnosis
bearing
