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

Currently, a common difficulty in diagnosing failures within Pratt & Whitney’s FlOO-PW-100/200 gas turbine engine occurs when a fault in one part of a system, comprising an engine, airframe, test cell, and Automated Ground Engine Test Set (AGETS) equipment, is manifested as an out-ofbounds parameter elsewhere in that 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 specifed 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 paper describes a diagnostic tool (Le., 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 (PW) and United Technologies Research Center (UTRC) using an Artificial Intelligence tool called Qualitative Reasoning System (QRS).