基于峭度准则EEMD及改进形态滤波方法的轴承故障诊断

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 fault diagnosis using EEMD and improved morphological filtering method based on kurtosis criterion

Bearing faults are always observed as cyclical impulses in the vibration signal. Detecting and extracting the impulse response signal is the primary feature extraction means of bearing faults diagnose. However, the impulse response is mostly immersed in strong noise, which makes it difficult to diagnose the bearing faults. In order to effectively remove this noise and detect the impulses, a hybrid method combining ensemble empirical mode decomposition (EEMD) method and an improved morphological filtering based on kurtosis criterion was proposed in this paper. In this method, a new decision strategy of intrinsic mode function (IMF) and morphological structure element (SE) is based on kurtosis criterion. The signal reconstructed by the selected IMFs is processed by improved morphological filtering based on kurtosis criterion. Meanwhile, it also avoids the selection of center frequency and filter band in resonance demodulation method and has good adaptability. When analyzed with the inner and outer ring faults of rolling bearing, the results show that this method can distinctly and accurately extract the fault information and the noise is well suppressed. So, it can be used to diagnose the bearing faults accurately.