Paper Info
Title
Iterative hybrid causal model based diagnosis: Application to automotive embedded functions.
Abstract
This paper addresses off-line diagnosis of embedded functions, such as that made in workshops by the technicians. The diagnosis problem expresses as the determination of a proper sequence of tests and measures at available control points, which would lead to greedily localize the fault quickly and at the lowest cost. Whereas anticipated discrete faults can be properly addressed by fault dictionary methods based on simulation, a consistency based method designed for hybrid systems is proposed to address parametric faults and non-anticipated faults. This method uses those same inputs as the fault dictionary method and the only additional information is the structure of the reference models in the form of a causal graph and the interpretation of the simulation results into qualitative values and events. The consistency based diagnosis method is combined with a test selection procedure to produce an original iterative diagnosis method for hybrid systems that reduces diagnosis ambiguity at each iteration.
Year
DOI
Venue
2015
10.1016/j.engappai.2014.09.016
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
Consistency based diagnosis,Hybrid systems,Causal graphs,Test selection,Automotive embedded functions
Test selection,Reference model,Computer science,Algorithm,Parametric statistics,Artificial intelligence,Hybrid system,Ambiguity,Machine learning,Windscreen wiper,Automotive industry,Causal model
Journal
Volume
ISSN
Citations 
37
0952-1976
1
PageRank 
References 
Authors
0.34
21
3
Name
Order
Citations
PageRank
Renaud Pons110.68
Audine Subias220.70
L. Trav&#233/-massuy&#232/s339454.06