{"title":"Reliability computing methods for TT&C system using Markov approach","authors":"Hua Yan, Xiaoyue Wu","doi":"10.1109/ICQR2MSE.2012.6246197","DOIUrl":null,"url":null,"abstract":"The reliability analysis of tracking, telemetry and command (TT&C) system is very important for performing space mission successfully. Essentially, the TT&C system can be regarded as a phased-mission system (PMS). In this paper, Markov approach is used to construct a distinct model for each phase of the system and a separate evaluation for each of these models. This paper shows: (1) a method to compute state probability vector of Markov model, which can achieve a good efficiency and accuracy; (2) states mapping mechanism, the rule of states mapping between two task phases is given, and mapping mechanism in three different cases is proposed. Finally, examples with numerical results show efficiency of our states probability vector computing algorithm and states mapping mechanism.","PeriodicalId":401503,"journal":{"name":"2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICQR2MSE.2012.6246197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The reliability analysis of tracking, telemetry and command (TT&C) system is very important for performing space mission successfully. Essentially, the TT&C system can be regarded as a phased-mission system (PMS). In this paper, Markov approach is used to construct a distinct model for each phase of the system and a separate evaluation for each of these models. This paper shows: (1) a method to compute state probability vector of Markov model, which can achieve a good efficiency and accuracy; (2) states mapping mechanism, the rule of states mapping between two task phases is given, and mapping mechanism in three different cases is proposed. Finally, examples with numerical results show efficiency of our states probability vector computing algorithm and states mapping mechanism.