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
During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper propo...During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.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.
基金supported by the National Natural Science Foundation of China(Grant No.U1636115)the PAPD fund+1 种基金the CICAEET fundthe Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2017BDKFJJ017)
文摘During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.
