M. Liao, Cui Lu, Hong Zhang, Sheng-Jie Wei, Ying Zheng
{"title":"具有时间序列约束的离散事件系统故障诊断","authors":"M. Liao, Cui Lu, Hong Zhang, Sheng-Jie Wei, Ying Zheng","doi":"10.1109/DDCLS.2017.8068087","DOIUrl":null,"url":null,"abstract":"Automata model based method is widely applied for fault diagnosis of discrete event systems. In practical systems, the occurrences of system events often have fixed order, and the faults may be reCoverable. The traditional automata model cannot handle these problems. In this paper, an automata model containing the information of time sequence is built, which will help to describe the system accurately and simplify the structure of the model. Based on this model, a diagnosis method is proposed to diagnose the faults, which searches for the observable events sequence of the system to obtain diagnosis results. An example indicates that the proposed method can reduce the number of diagnose paths and save diagnosis time compared with the traditional method.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault diagnosis of discrete event systems with time sequence constraint\",\"authors\":\"M. Liao, Cui Lu, Hong Zhang, Sheng-Jie Wei, Ying Zheng\",\"doi\":\"10.1109/DDCLS.2017.8068087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automata model based method is widely applied for fault diagnosis of discrete event systems. In practical systems, the occurrences of system events often have fixed order, and the faults may be reCoverable. The traditional automata model cannot handle these problems. In this paper, an automata model containing the information of time sequence is built, which will help to describe the system accurately and simplify the structure of the model. Based on this model, a diagnosis method is proposed to diagnose the faults, which searches for the observable events sequence of the system to obtain diagnosis results. An example indicates that the proposed method can reduce the number of diagnose paths and save diagnosis time compared with the traditional method.\",\"PeriodicalId\":419114,\"journal\":{\"name\":\"2017 6th Data Driven Control and Learning Systems (DDCLS)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 6th Data Driven Control and Learning Systems (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS.2017.8068087\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 6th Data Driven Control and Learning Systems (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2017.8068087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault diagnosis of discrete event systems with time sequence constraint
Automata model based method is widely applied for fault diagnosis of discrete event systems. In practical systems, the occurrences of system events often have fixed order, and the faults may be reCoverable. The traditional automata model cannot handle these problems. In this paper, an automata model containing the information of time sequence is built, which will help to describe the system accurately and simplify the structure of the model. Based on this model, a diagnosis method is proposed to diagnose the faults, which searches for the observable events sequence of the system to obtain diagnosis results. An example indicates that the proposed method can reduce the number of diagnose paths and save diagnosis time compared with the traditional method.