{"title":"基于敏感特征选择和非线性特征融合的滚动轴承故障诊断方法","authors":"Peng Liu, Hong-ru Li, P. Ye","doi":"10.1109/ICICTA.2015.17","DOIUrl":null,"url":null,"abstract":"To solve the problem that there were non-sensitive features and over-high dimensions in the feature set of fault diagnosis, a new feature extraction method based on sensitive feature selection and nonlinear feature fusion for rolling element bearing fault diagnosis was proposed. CDET was utilized to choose features sensitive to fault severity from the high dimensional feature set, and weighted the selected sensitive features by their sensitive degree. The weighted sensitive feature subset was compressed with LPP to reduce its dimensions and get the compressed more sensitive feature subset, which can enhance the discrimination between all classes by improving the between-class scatter and within-class scatter for fault classification. Compared with the classification result of the original high dimensional feature set based on direct LPP without feature selection, the classification result of the proposed method has the advantages of higher compactness and higher computational efficiency.","PeriodicalId":231694,"journal":{"name":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Method for Rolling Bearing Fault Diagnosis Based on Sensitive Feature Selection and Nonlinear Feature Fusion\",\"authors\":\"Peng Liu, Hong-ru Li, P. Ye\",\"doi\":\"10.1109/ICICTA.2015.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To solve the problem that there were non-sensitive features and over-high dimensions in the feature set of fault diagnosis, a new feature extraction method based on sensitive feature selection and nonlinear feature fusion for rolling element bearing fault diagnosis was proposed. CDET was utilized to choose features sensitive to fault severity from the high dimensional feature set, and weighted the selected sensitive features by their sensitive degree. The weighted sensitive feature subset was compressed with LPP to reduce its dimensions and get the compressed more sensitive feature subset, which can enhance the discrimination between all classes by improving the between-class scatter and within-class scatter for fault classification. Compared with the classification result of the original high dimensional feature set based on direct LPP without feature selection, the classification result of the proposed method has the advantages of higher compactness and higher computational efficiency.\",\"PeriodicalId\":231694,\"journal\":{\"name\":\"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICTA.2015.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICTA.2015.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Method for Rolling Bearing Fault Diagnosis Based on Sensitive Feature Selection and Nonlinear Feature Fusion
To solve the problem that there were non-sensitive features and over-high dimensions in the feature set of fault diagnosis, a new feature extraction method based on sensitive feature selection and nonlinear feature fusion for rolling element bearing fault diagnosis was proposed. CDET was utilized to choose features sensitive to fault severity from the high dimensional feature set, and weighted the selected sensitive features by their sensitive degree. The weighted sensitive feature subset was compressed with LPP to reduce its dimensions and get the compressed more sensitive feature subset, which can enhance the discrimination between all classes by improving the between-class scatter and within-class scatter for fault classification. Compared with the classification result of the original high dimensional feature set based on direct LPP without feature selection, the classification result of the proposed method has the advantages of higher compactness and higher computational efficiency.