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为了避免经验模式分解(EMD)过程中不同时间尺度函数间的模式混叠,采用基于高斯白噪声加入的经验模式分解方法,并将之应用于旋转机械故障诊断中。该方法主要是对同一信号重复若干次加入相互独立的高斯白噪声序列,并分别筛选固有模式函数,而后把各个对应的固有模式函数进行平均计算,消除白噪声对分解结果的影响。最后,通过对固有模式函数进行包络解调,从中提取故障特征。对实际旋转机械故障振动信号的分析结果表明,该方法能有效避免固有模式函数间模式混叠,提高故障诊断效果。
In order to avoid the pattern aliasing between different time-scale functions in empirical mode decomposition (EMD), an empirical mode decomposition method based on Gaussian white noise is introduced and applied to the fault diagnosis of rotating machinery. The method mainly includes adding several mutually independent Gaussian white noise sequences to the same signal several times and filtering the intrinsic mode functions respectively, and then calculating the average of each corresponding intrinsic mode function to eliminate the effect of white noise on the decomposition result. Finally, the fault feature is extracted by envelope demodulation of the intrinsic mode function. The analysis results of vibration signals of actual rotating machinery show that this method can effectively avoid mode aliasing between intrinsic mode functions and improve the fault diagnosis effect.