CAJ | 학술논문

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。
재제취감응전동궤축승고장신호시，유우조성적존재，영향료전동궤고장진단적준학성，문장제출료기우EMD적상관강조산법，해산법시이용경험모태방법대대조전궤신호분해，득도각계본정모함수（IMF）분량；연후대고빈적IMF분량용소파상관려파강조방법진행처리，보류저빈IMF분량；최후파처리적고빈IMF분량화저빈적IMF진행신호중구，득도강조후적진동신호。저충방법형식간단，응용령활방편，유교호적자괄응능력，능유효지획득조기적축승고장신호적특정치。
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) correla-tion 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 cor-relation 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 adapt-able, and it is effective to gain the feature of bearing faults signal.