{"title":"基于贝叶斯网络的水下航行器软件故障诊断模型及其简化方法","authors":"Changting Shi, Ru-bo Zhang","doi":"10.1109/WCICA.2011.5970682","DOIUrl":null,"url":null,"abstract":"According to the uncertainty broadly existed in fault diagnosis of AUV software system, this paper presents a Bayesian Networks diagnosis model with three layers based on CME. On the basis of that, the paper also presents a cutting irrelative node method based on task context according to AUV's specific nature, this method predigests network, reduces the complexity of consequence calculation, and enhances the ability of real-time fault diagnosis effectively.","PeriodicalId":211049,"journal":{"name":"2011 9th World Congress on Intelligent Control and Automation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Software fault diagnosis model of AUV based on Bayesian Networks and its simplified method\",\"authors\":\"Changting Shi, Ru-bo Zhang\",\"doi\":\"10.1109/WCICA.2011.5970682\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"According to the uncertainty broadly existed in fault diagnosis of AUV software system, this paper presents a Bayesian Networks diagnosis model with three layers based on CME. On the basis of that, the paper also presents a cutting irrelative node method based on task context according to AUV's specific nature, this method predigests network, reduces the complexity of consequence calculation, and enhances the ability of real-time fault diagnosis effectively.\",\"PeriodicalId\":211049,\"journal\":{\"name\":\"2011 9th World Congress on Intelligent Control and Automation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 9th World Congress on Intelligent Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCICA.2011.5970682\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 9th World Congress on Intelligent Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCICA.2011.5970682","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Software fault diagnosis model of AUV based on Bayesian Networks and its simplified method
According to the uncertainty broadly existed in fault diagnosis of AUV software system, this paper presents a Bayesian Networks diagnosis model with three layers based on CME. On the basis of that, the paper also presents a cutting irrelative node method based on task context according to AUV's specific nature, this method predigests network, reduces the complexity of consequence calculation, and enhances the ability of real-time fault diagnosis effectively.
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