Name
Title | ||
|---|---|---|
Graphical Inference Methods for Fault Diagnosis based on Information from Unreliable Sensors |
Abstract | ||
|---|---|---|
In this paper, we study the application of decoding algorithms to the multiple fault diagnosis (MFD) problem. Prompted by the resemblance between graphical representations for MFD problems and parity check codes, we develop a suboptimal iterative belief propagation algorithm (BPA) that is based on the graphical inference method for low density parity check codes. Our simulation results suggest that the algorithm performance strongly depends on the connection density and the reliability of the alarm network. In particular, when the connection density is low and when the alarms and/or connections are unreliable, the algorithm performs almost optimally, i.e., it converges to the solution with the highest posterior probability most of the times. We also provide analytical bounds on the performance of the algorithm for special classes of systems in our framework |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICARCV.2006.345228 | ICARCV |
Keywords | Field | DocType |
multiple fault diagnosis,belief propagation,low density parity check codes,suboptimal iterative belief propagation algorithm,posterior probability,connection density,graphical inference,fault diagnosis,belief maintenance,decoding algorithm,graph theory,alarm network reliability,error statistics,parity check codes,alarm correlation,unreliable sensors | Graph theory,Parity bit,Low-density parity-check code,Inference,Computer science,Control theory,ALARM,Algorithm,Theoretical computer science,Posterior probability,Decoding methods,Belief propagation | Conference |
ISSN | ISBN | Citations |
2474-2953 | 1-4214-042-1 | 4 |
PageRank | References | Authors |
0.52 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tung Le | 1 | 37 | 5.87 |
| Christoforos N. Hadjicostis | 2 | 1425 | 127.48 |