{"title":"一种具有干扰解耦的数据驱动过程监测方法*","authors":"Hao Luo, Kuan Li, M. Huo, Shen Yin, O. Kaynak","doi":"10.1109/DDCLS.2018.8515978","DOIUrl":null,"url":null,"abstract":"This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. The performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"569-574"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Data-Driven Process Monitoring Approach with Disturbance Decoupling*\",\"authors\":\"Hao Luo, Kuan Li, M. Huo, Shen Yin, O. Kaynak\",\"doi\":\"10.1109/DDCLS.2018.8515978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. The performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.\",\"PeriodicalId\":6565,\"journal\":{\"name\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"volume\":\"18 1\",\"pages\":\"569-574\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS.2018.8515978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2018.8515978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Data-Driven Process Monitoring Approach with Disturbance Decoupling*
This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. The performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.
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