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Fusion of Gas Turbines Diagnostic Inference: The Dempster-Schafer Approach

[+] Author Affiliations
C. Romessis, A. Kyriazis, K. Mathioudakis

National Technical University of Athens, Athens, Greece

Paper No. GT2007-27043, pp. 505-513; 9 pages
  • ASME Turbo Expo 2007: Power for Land, Sea, and Air
  • Volume 1: Turbo Expo 2007
  • Montreal, Canada, May 14–17, 2007
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4790-X | eISBN: 0-7918-3796-3
  • Copyright © 2007 by ASME


This paper proposes a fusion technique allowing the merge of conclusions provided by diagnostic methods that act independently for the detection of gas turbine faults. The proposed technique adopts the principles of Dempster-Schafer theory for the fusion of two diagnostic methods output; these are the method of Bayesian Belief Networks (BBN) and the method of Probabilistic Neural Networks (PNN). The proposed technique has been applied for the detection of thermodynamic as well as mechanical faults on gas turbines. First, the case of a turbofan engine of civil aviation is examined. The proposed technique allows the fusion of diagnostic inference on the presence of several faults of thermodynamic nature. Then the case of a radial and an axial compressor are examined, where several mechanical faults are deliberately implemented. In all cases, the effectiveness of the proposed fusion technique demonstrates that the merge of diagnostic information from different sources leads to better and safer diagnosis.

Copyright © 2007 by ASME
Topics: Gas turbines



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