【摘要】 在提取感应电动机轴承故障信号时,由于噪声的存在,影响了电动机故障诊断的准确性,文章提出了基于EMD的相关降噪算法,该算法是利用经验模态方法对带噪电机信号分解,得到各阶本征模函数(IMF)分量;然后对高频的IMF分量用小波相关滤波降噪方法进行处理,保留低频IMF分量;最后把处理的高频IMF分量和低频的IMF进行信号重构,得到降噪后的振动信号。这种方法形式简单,应用灵活方便,有较好的自适应能力,能有效地获得早期的轴承故障信号的特征值。更多还原

【Abstract】 When the electric motor bearing faults features are extracted, the existence of lots of noise reduces the accuracy of fault diagnosis. To solve this problem, an EMD(Empirical Mode Decomposition) correlation de-noising algorithm is proposed. EMD is used to decompose the electric motor vibration signal with noise to obtain each intrinsic mode function(IMF). The high frequency IMF is de-noised by a wavelet correlation filter, and the low frequency IMF is retained. Finally, the high frequency and the low frequency IMF can be reconstructed to obtain the de-noised signal. The proposed method is simple, flexible and adaptable, and it is effective to gain the feature of bearing faults signal.更多还原