【摘要】 针对滚动轴承振动信号非线性、非平稳性以及故障难以识别的问题,提出了一种经验小波变换(EWT)、奇异值熵和t分布随机领域嵌入(t-SNE)相结合的滚动轴承故障识别方法。对原始振动信号进行EWT分解得到若干固有模态分量(IMF),对IMF进行奇异值分解求取奇异值熵。利用t-SNE算法对奇异值熵组成的特征矩阵进行降维,所提取的低维特征能够有效反映故障信息。最后,将低维特征输入到Kmeans分类器中进行模式识别。将该方法应用到滚动轴承实验中并与EMD+奇异值熵+t-SNE、EWT+奇异值熵+PCA方法进行对比,结果表明:所提方法能够更有效地提取滚动轴承的故障特征,提高了故障识别的精度。更多还原

【Abstract】 Aiming at the problems of nonlinearity,non-stationarity and difficult identification of rolling bearing vibration signals,a fault identification method for rolling bearings based on empirical wavelet transform( EWT),singular value entropy and t-distributed stochastic neighbor embedding( t-SNE) is proposed. The method performs EWT decomposition on the original vibration signal to obtain several intrinsic modal function( IMF),and performs singular value decomposition on the IMF to obtain singular value entropy. The t-SNE algorithm is used to reduce the feature matrix composed of singular value entropy,and the extracted low-dimensional features can effectively reflect the fault information. Finally,low-dimensional features are input into the K-means classifier for pattern recognition. The method is applied to rolling bearing experiments and compared with EMD + singular value entropy + t-SNE,EWT + singular value entropy + PCA method. The results show that the method can extract the fault characteristics of rolling bearings more effectively and improve the accuracy of fault identification.更多还原