Name
Abstract | ||
|---|---|---|
Fault diagnosis is a crucial and challenging task in the automatic control of complex systems, whose efficiency depends on the diagnosability property of a system. Diagnosability describes the system ability to determine whether a given fault has effectively occurred based on the observations. However, this is a very strong property that requires generally high number of sensors to be satisfied. Consequently, it is not rare that developing a diagnosable system is too expensive. To solve this problem, in this paper, we first define a new system property called manifestability that represents the weakest requirement on faults and observations for having a chance to identify on line fault occurrences and can be verified at design stage. Then, we propose an algorithm with PSPACE complexity to automatically verify it. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-672-9-1718 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Event tree,Computer science,Artificial intelligence,Machine learning | Conference | 285 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lina Ye | 1 | 26 | 8.75 |
| Philippe Dague | 2 | 248 | 28.20 |
| Delphine Longuet | 3 | 64 | 7.82 |
| Laura Brandán Briones | 4 | 75 | 4.99 |
| Agnes Madalinski | 5 | 1 | 1.03 |