AGETS MBR An Application of Model-Based Reasoning to Gas Turbine Diagnostics

Authors

  • Howard A. Winston
  • Robert T. Clark
  • Gene Buchina

DOI:

https://doi.org/10.1609/aimag.v16i4.1172

Abstract

A common difficulty in diagnosing failures within Pratt & Whitney's F100-PW-100/200 gas turbine engine occurs when a fault in one part of a system -- comprising an engine, an airframe, a test cell, and automated ground engine test set (AGETS) equipment -- is manifested as an out-of-bound parameter elsewhere in the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the specified local parameter, it will pass, leaving only the operators' experience and traditional fault-isolation manuals to locate the source of the problem in another part of the system. This article describes a diagnostic tool (that is, AGETS MBR), designed to overcome this problem by isolating failures using an overall system troubleshooting approach. AGETS MBR was developed jointly by personnel at Pratt & Whitney and United Technologies Research Center using an AI tool called the qualitative reasoning system (QRS).

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Published

1995-12-15

How to Cite

Winston, H. A., Clark, R. T., & Buchina, G. (1995). AGETS MBR An Application of Model-Based Reasoning to Gas Turbine Diagnostics. AI Magazine, 16(4), 67. https://doi.org/10.1609/aimag.v16i4.1172

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Articles