{"title":"过程诊断问题中Zakai方程的解","authors":"K. Loparo, Z. Roth","doi":"10.1109/CDC.1984.272083","DOIUrl":null,"url":null,"abstract":"Fault detection and diagnosis is modelled as the nonlinear filtering problem of a linear systems with jumping parameters. Assuming that faults occur according to a stationary finite state Markov process, the optimal filter is represented in terms of a vector Zakai equation. This equation can be solved explicitly in a manner that reveals both the realization as well as the physical meaning of several suboptimal filtering schemes.","PeriodicalId":269680,"journal":{"name":"The 23rd IEEE Conference on Decision and Control","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1984-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the solution of the Zakai equation for the process diagnostics problem\",\"authors\":\"K. Loparo, Z. Roth\",\"doi\":\"10.1109/CDC.1984.272083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fault detection and diagnosis is modelled as the nonlinear filtering problem of a linear systems with jumping parameters. Assuming that faults occur according to a stationary finite state Markov process, the optimal filter is represented in terms of a vector Zakai equation. This equation can be solved explicitly in a manner that reveals both the realization as well as the physical meaning of several suboptimal filtering schemes.\",\"PeriodicalId\":269680,\"journal\":{\"name\":\"The 23rd IEEE Conference on Decision and Control\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 23rd IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1984.272083\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 23rd IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1984.272083","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the solution of the Zakai equation for the process diagnostics problem
Fault detection and diagnosis is modelled as the nonlinear filtering problem of a linear systems with jumping parameters. Assuming that faults occur according to a stationary finite state Markov process, the optimal filter is represented in terms of a vector Zakai equation. This equation can be solved explicitly in a manner that reveals both the realization as well as the physical meaning of several suboptimal filtering schemes.