东南大学学报（英文版）2019，Vol.35Issue(4)：408-416,9.DOI:10.3969/j.issn.1003-7985.2019.04.002
基于VMD和嵌入选择NPE的滚动轴承性能退化评估
Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE
摘要
Abstract
In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding (ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition (VMD),and the singular value and relative energy of each intrinsic mode function (IMF) are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and beating performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis (PCA) and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.关键词
性能退化评估/变分模态分解(VMD)/邻域保持嵌入(NPE)/支持向量数据描述(SVDD)Key words
performance degradation evaluation/variational mode decomposition (VMD)/neighborhood preserving embedding (NPE)/support vector data description(SVDD)分类
机械制造引用本文复制引用
童清俊,胡建中,贾民平,许飞云..基于VMD和嵌入选择NPE的滚动轴承性能退化评估[J].东南大学学报（英文版）,2019,35(4):408-416,9.基金项目
The National Natural Science Foundation of China(No.51975117). （No.51975117）
