dc.contributor.author | Sahin, Ferat | |
dc.contributor.author | Yavuz, M. Cetin | |
dc.contributor.author | Arnavut, Ziya | |
dc.contributor.author | Uluyol, Onder | |
dc.date.accessioned | 2009-04-08T15:11:13Z | |
dc.date.available | 2009-04-08T15:11:13Z | |
dc.date.issued | 2007-03 | |
dc.identifier.uri | http://hdl.handle.net/1850/8969 | |
dc.description | RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/ | |
dc.description.abstract | This paper presents a fault diagnosis system for airplane engines using Bayesian networks (BN) and distributed particle
swarm optimization (PSO). The PSO is inherently parallel, works for large domains and does not trap into local maxima.
We implemented the algorithm on a computer cluster with 48 processors using message passing interface (MPI) in Linux.
Our implementation has the advantages of being general, robust, and scalable. Unlike existing BN-based fault diagnosis
methods, neither expert knowledge nor node ordering is necessary prior to the Bayesian Network discovery. The raw datasets
obtained from airplane engines during actual flights are preprocessed using equal frequency binning histogram and
used to generate Bayesian networks fault diagnosis for the engines. We studied the performance of the distributed PSO
algorithm and generated a BN that can detect faults in the test data successfully. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartofseries | Vol. 33 | en_US |
dc.relation.ispartofseries | Issue 2 | en_US |
dc.subject | Bayesian networks | en_US |
dc.subject | Fault diagnosis | en_US |
dc.subject | Parallel computing | en_US |
dc.subject | Particle swarm | en_US |
dc.title | Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization | en_US |
dc.type | Article | en_US |
dc.identifier.url | http://dx.doi.org/10.1016/j.parco.2006.11.005 | |