Open AccessAGETS MBR: An Application of Model‐Based Reasoning to Gas Turbine Diagnostics
Author(s) -
Winston Howard A.,
Clark Robert T.,
Buchina Gene
Publication year - 1995
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v16i4.1172
Subject(s) - troubleshooting , fault (geology) , reliability engineering , airframe , engineering , set (abstract data type) , fault detection and isolation , gas turbines , test (biology) , semantic reasoner , computer science , control engineering , artificial intelligence , mechanical engineering , actuator , aerospace engineering , paleontology , seismology , geology , biology , programming language
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 ).