Evaluation of testing assignment for system level self-diagnosis

2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP)(2016)

引用 1|浏览0
暂无评分
摘要
The paper concerns system level self-diagnosis (SLSD). SLSD aims at diagnosing systems composed by units with the requirement that they are able to test each other by exchanging information through available links. At this level of diagnosis, each particular test is considered as atomic. It means that the details of a test are abstracted (not considered), and only the result of test is taken into consideration. One of the main issues of SLSD is the issue of testing assignment that defines the possible set of tests among the system units. System testing assignment relies and depends on physical connections among the system units. The issue of testing assignment is tightly connected with the diagnosability problem of SLSD. Diagnosability problem of SLSD is the problem of how to determine the family of fault sets that a given testing assignment can diagnose for some fault model. In the paper, we have shown how different testing assignments can be evaluated and compared. For this, we suggest to use characteristic numbers. We also have shown how these characteristic numbers can be computed.
更多
查看译文
关键词
system level self-diagnosis,diagnosing systems,testing assignments
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要