摘要
针对滚动轴承原始数据集包含高维非敏感特征的问题,提出一种集成核主成分分析(Kernel Principal Component Analysis,KPCA)与t‑分布随机邻域嵌入(t‑distributed Stochastic Neighbor Embedding,t‑SNE)的滚动轴承故障低维敏感特征提取方法。该方法先计算滚动轴承原始振动信号的时域、频域以及时频域特征,构建初始高维特征数据集。利用KPCA降低高维数据集的相关性,在最大化高维数据全局特征方差的目标下,提取出非线性特征子集。通过t‑SNE充分挖掘故障特征数据集的局部结构信息,进一步获取具有高判别性的低维敏感特征子集。将低维特征子集输入到k‑近邻分类器(k‑nearest Neighbor Classifier,KNNC)进行分类,以分类准确率和聚类结果作为度量指标,对特征提取结果的优劣予以评价。上述过程综合考虑了数据集的全局和局部结构特征,充分利用了数据自身的结构信息,从而可准确提取其低维敏感特征。将该方法用于滚动轴承故障诊断实验中,通过与其他典型特征提取方法进行对比,及其对含噪情况下轴承故障特征的准确提取,验证了方法的有效性。
The original data set of rolling bearing contains high-dimensional non-sensitive features,so a low-dimensional sensitive feature extraction method through integrating kernel principal component analysis(KPCA)and t-distributed stochastic neighbor embedding(t-SNE)is proposed in this paper.The time domain,frequency domain and time-frequency domain features of the raw rolling bearing vibration signal are calculated to construct the original high-dimensional feature data set.KPCA is used to reduce the correlation of high-dimensional data set,and the nonlinear feature subset is extracted while maximizing the global feature variance of the data set.t-SNE is employed to mine the local structure information of feature data set,and further obtain the low-dimensional sensitive feature subset with high discriminability.The low-dimensional feature subset is input into the k-nearest neighbor classifier(KNNC),and the classification accuracy and clustering results are used as the quantitative indexes to evaluate the performance of the feature extraction method.In this process,the global and local structure features of the data set are comprehensively considered and the structure information of the data itself is fully utilized,so the low-dimensional sensitive features can be accurately extracted.The proposed method is applied to the fault diagnosis of a rolling bearing test rig.By comparing the results obtained from 6 different feature extraction methods and analyzing the fault data sets under variable rotating speeds and different random noises respectively,the effectiveness of the proposed method is verified,and consequently it is actually an excellent feature extraction method for the fault recognition of rolling bearings.
作者
王望望
邓林峰
赵荣珍
吴耀春
WANG Wang-wang;DENG Lin-feng;ZHAO Rong-zhen;WU Yao-chun(School of Mechanical and Electronical Engineering,Lanzhou University of Technology,Lanzhou 730050,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2021年第2期431-440,共10页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(51675253)
中国博士后科学基金资助项目(2016M592857)
甘肃省自然科学基金资助项目(1610RJZA004)
兰州理工大学红柳一流学科建设项目。
关键词
故障诊断
滚动轴承
故障特征提取
核主成分分析
t‑分布随机邻域嵌入
k‑近邻分类器
fault diagnosis
rolling bearing
fault feature extraction
kernel principal component analysis
t‑distribution stochastic neighbor embedding
k‑nearest neighbor classifier
