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。
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.
作者
段萍
王旭
丁承君
冯玉伯
秦越
DUAN Ping;WANG Xu;DING Chengjun;FENG Yubo;QIN Yue(College of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China)
出处
《传感器与微系统》
CSCD
北大核心
2021年第3期134-137,共4页
Transducer and Microsystem Technologies
基金
河北省科技计划资助项目(14214902D)。
