0
Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

A Real-World Application of Fuzzy Logic and Influence Coefficients for Gas Turbine Performance Diagnostics

[+] Author and Article Information
Richard W. Eustace

 Defence Science and Technology Organisation, 506 Lorimer Street, Fishermens Bend, Victoria 3207, Australiarichard.eustace@dsto.defence.gov.au

This is implemented via the standard MATLAB “lsqnonneg” function.

J. Eng. Gas Turbines Power 130(6), 061601 (Aug 26, 2008) (9 pages) doi:10.1115/1.2940989 History: Received May 02, 2007; Revised January 06, 2008; Published August 26, 2008

This paper presents an example of the use of fuzzy logic combined with influence coefficients applied to engine test-cell data to diagnose gas-path related performance faults. The approach utilizes influence coefficients, which describe the changes in measurable parameters due to changes in component condition such as compressor efficiency. Such approaches usually have the disadvantages of attributing measurement noise or sensor errors to changes in engine condition and do not have the ability to diagnose more faults than the number of measurement parameters that exist. These disadvantages usually make such methods impractical for anything but simulated data without measurement noise or errors. However, in this example, the influence coefficients are used in an iterative approach, in combination with fuzzy logic, to overcome these obstacles. The method is demonstrated using eight examples from real-world test-cell data.

FIGURES IN THIS ARTICLE
<>
Copyright © 2008 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Engine configuration and maintenance costs and times

Grahic Jump Location
Figure 2

Schematic of the AutoStack diagnostic process

Grahic Jump Location
Figure 3

Membership function for fault probability categories

Grahic Jump Location
Figure 8

Symptom of Case 7 (HPT NGV erosion) compared to engine-to-engine variation

Grahic Jump Location
Figure 7

Symptom of Case 2 (LPT untwist) compared to engine-to-engine variation

Grahic Jump Location
Figure 6

Standard deviation of the engine-to-engine parameter variation compared to the performance change for Case 1

Grahic Jump Location
Figure 5

Membership function for fault severity

Grahic Jump Location
Figure 4

Membership function for fault count

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In