Wenbing Zhu, Guangzao Huang, Jinting Guan, Guoli Ji, Sun Zhou
{"title":"复杂工业过程中贝叶斯方法与LDA特征提取的联合故障诊断","authors":"Wenbing Zhu, Guangzao Huang, Jinting Guan, Guoli Ji, Sun Zhou","doi":"10.1109/ISNE.2016.7543350","DOIUrl":null,"url":null,"abstract":"Bayesian method is a class of data-driven fault diagnosis method which is a topic of significant practical interest. In order to improve diagnosis accuracy and reduce computation load, linear discriminant analysis (LDA) is employed to extract features before performing Bayesian diagnosis. It can maximize the explicit function to achieve the goal that within-class data points as close as possible and between-class data points as far as possible. Tennessee Eastman Challenge (TE) is utilized to verify the effectiveness of the proposed method.","PeriodicalId":127324,"journal":{"name":"2016 5th International Symposium on Next-Generation Electronics (ISNE)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fault diagnosis of joint Bayesian method and LDA feature extraction in complicated industrial process\",\"authors\":\"Wenbing Zhu, Guangzao Huang, Jinting Guan, Guoli Ji, Sun Zhou\",\"doi\":\"10.1109/ISNE.2016.7543350\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Bayesian method is a class of data-driven fault diagnosis method which is a topic of significant practical interest. In order to improve diagnosis accuracy and reduce computation load, linear discriminant analysis (LDA) is employed to extract features before performing Bayesian diagnosis. It can maximize the explicit function to achieve the goal that within-class data points as close as possible and between-class data points as far as possible. Tennessee Eastman Challenge (TE) is utilized to verify the effectiveness of the proposed method.\",\"PeriodicalId\":127324,\"journal\":{\"name\":\"2016 5th International Symposium on Next-Generation Electronics (ISNE)\",\"volume\":\"126 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 5th International Symposium on Next-Generation Electronics (ISNE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISNE.2016.7543350\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 5th International Symposium on Next-Generation Electronics (ISNE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISNE.2016.7543350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault diagnosis of joint Bayesian method and LDA feature extraction in complicated industrial process
Bayesian method is a class of data-driven fault diagnosis method which is a topic of significant practical interest. In order to improve diagnosis accuracy and reduce computation load, linear discriminant analysis (LDA) is employed to extract features before performing Bayesian diagnosis. It can maximize the explicit function to achieve the goal that within-class data points as close as possible and between-class data points as far as possible. Tennessee Eastman Challenge (TE) is utilized to verify the effectiveness of the proposed method.