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Combining Bayesian Belief Networks With Gas Path Analysis for Test Cell Diagnostics and Overhaul FREE

[+] Author Affiliations
Carl A. Palmer

Enter Software Inc., Davis, CA

Paper No. 98-GT-168, pp. V005T15A015; 9 pages
doi:10.1115/98-GT-168
From:
  • ASME 1998 International Gas Turbine and Aeroengine Congress and Exhibition
  • Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education
  • Stockholm, Sweden, June 2–5, 1998
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-7866-8
  • Copyright © 1998 by ASME

abstract

Engine overhaul shops need a reliable analytical methodology to pinpoint the cause(s) of engine test-cell under-performance to aid the overhaul decision-making process. Gas path analysis codes have been somewhat successful, but have not been entirely satisfactory. Previous works [Doel, 1994] have raised the idea that if other information could be integrated with the gas path analysis results, it may be possible to achieve better results.

This paper presents a diagnostic system developed for the CF6 family of engines. The system integrates test cell measurements and the gas path analysis program results with information regarding engine operational history, build-up workscope, and direct physical observations in a Bayesian belief network. The paper lays out the nature of the problem and the system requirements and design.

The system produces a diagnosis while following a cost-effective diagnostic process using value of information calculations. This is illustrated through sample cases.

Copyright © 1998 by ASME
This article is only available in the PDF format.

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