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Automated Fault Diagnosis of a Micro Turbine With Comparison to a Neural Network Technique

[+] Author Affiliations
Craig R. Davison, A. M. Birk

Queen’s University, Kingston, ON, Canada

Paper No. GT2006-91085, pp. 795-804; 10 pages
  • ASME Turbo Expo 2006: Power for Land, Sea, and Air
  • Volume 2: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Controls, Diagnostics and Instrumentation; Environmental and Regulatory Affairs
  • Barcelona, Spain, May 8–11, 2006
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4237-1 | eISBN: 0-7918-3774-2
  • Copyright © 2006 by ASME


In the predicted future of distributed power generation, a large number of users will operate gas turbine powered cogeneration systems. These systems will be small, relatively inexpensive, and installed in locations without ready access to experts in gas turbine maintenance. Consequently, an automated system to monitor the engine and diagnose the health of the system is required. To remain compatible with the low cost of the overall system, the diagnostic system must also be relatively inexpensive to install and operate. Therefore, a minimum number of extra sensors and computing power should be used. A statistical technique is presented that compares the engine operation over time to the expected trends for particular faults. The technique ranks the probability that each fault is occurring on the engine. The technique can be used online, with daily data from the engine forming a trend for comparison, or, with less accuracy, based on a single operating point. The use of transient operating data with this technique is also examined. This technique has the advantage of providing an automated numerical result of the probability of a particular mode of degradation occurring, but can also produce visual plots of the engine operation. This allows maintenance staff to remain involved in the process, if they wish, rather than the system operating purely as a black box, and provides an easy to understand aid for discussions with operators. The technique is compared to an off the shelf neural network to determine its usefulness in comparison to other diagnostic methods. The test bed was a micro turbojet engine. The data to test the system was obtained from both experiment and computer modeling of the test engine.

Copyright © 2006 by ASME



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