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(E...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 bearing 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.展开更多
针对传统方法对非线性或多模态间歇过程的故障检测率低的问题,提出一种基于K近邻邻域保持嵌入得分差分(difference of K nearest neighbors score associated with neighborhood preserving embedding,DKNPE)的健康状态监视方法。首先,...针对传统方法对非线性或多模态间歇过程的故障检测率低的问题,提出一种基于K近邻邻域保持嵌入得分差分(difference of K nearest neighbors score associated with neighborhood preserving embedding,DKNPE)的健康状态监视方法。首先,通过NPE方法计算训练数据集的得分矩阵,称其为样本的本质得分。然后,在训练数据集计算每个样本的K近邻均值,并将其投影到低维空间以获得样本的估计得分。接下来,在差分子空间(diffe-rence subspaces,DS)和差分残差子空间(difference residual subspaces,DRS)中分别建立两个新的统计量对样本进行过程监控。将本方法在两个模拟数值例子和半导体蚀刻过程中进行测试,并与PCA、FD-KNN和NPE等传统方法进行对比分析,测试结果验证了该方法的有效性。展开更多
邻域保持嵌入(Neighborhood Preserving Embedding,NPE),作为局部线性嵌入(Locally Linear Embedding,LLE)的线性化版本,由于在映射前后保持了数据的局部几何结构并得到了原始数据的子空间描述,在模式识别领域具有较强的应用价值。但作...邻域保持嵌入(Neighborhood Preserving Embedding,NPE),作为局部线性嵌入(Locally Linear Embedding,LLE)的线性化版本,由于在映射前后保持了数据的局部几何结构并得到了原始数据的子空间描述,在模式识别领域具有较强的应用价值。但作为非监督处理算法,在具体的模式分类中有一定局限性,提出一种NPE的改进算法——半监督判别邻域嵌入(SSDNE)算法,引入标记后样本点的类别信息,并在正则项中引入样本的流形结构,最大化标记样本点的类间信息和类内信息。既增加了算法的辨别能力又减少了监督算法中对样本点进行全标记的工作量。在ORL和YaleB人脸库上的实验结果表明,改进的算法较PCA、LDA、LPP以及原保持近邻判别嵌入算法的识别性能有了较明显的改善。展开更多
基金The National Natural Science Foundation of Chin(No.51975117)
文摘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 bearing 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.
文摘针对传统方法对非线性或多模态间歇过程的故障检测率低的问题,提出一种基于K近邻邻域保持嵌入得分差分(difference of K nearest neighbors score associated with neighborhood preserving embedding,DKNPE)的健康状态监视方法。首先,通过NPE方法计算训练数据集的得分矩阵,称其为样本的本质得分。然后,在训练数据集计算每个样本的K近邻均值,并将其投影到低维空间以获得样本的估计得分。接下来,在差分子空间(diffe-rence subspaces,DS)和差分残差子空间(difference residual subspaces,DRS)中分别建立两个新的统计量对样本进行过程监控。将本方法在两个模拟数值例子和半导体蚀刻过程中进行测试,并与PCA、FD-KNN和NPE等传统方法进行对比分析,测试结果验证了该方法的有效性。
文摘邻域保持嵌入(Neighborhood Preserving Embedding,NPE),作为局部线性嵌入(Locally Linear Embedding,LLE)的线性化版本,由于在映射前后保持了数据的局部几何结构并得到了原始数据的子空间描述,在模式识别领域具有较强的应用价值。但作为非监督处理算法,在具体的模式分类中有一定局限性,提出一种NPE的改进算法——半监督判别邻域嵌入(SSDNE)算法,引入标记后样本点的类别信息,并在正则项中引入样本的流形结构,最大化标记样本点的类间信息和类内信息。既增加了算法的辨别能力又减少了监督算法中对样本点进行全标记的工作量。在ORL和YaleB人脸库上的实验结果表明,改进的算法较PCA、LDA、LPP以及原保持近邻判别嵌入算法的识别性能有了较明显的改善。
