Abstract
Current diagnostics on most gas turbine engines involve o ine processing only. Since failures can cause serious safety and e ciency problems, such as elevated turbine temperatures or compressor stall, it is desirable to diagnose problems in as close to real-time as possible. Here, the implementation of a Bayesian network to engine fault diagnostics is demonstrated. Then a fuzzy diagnostic system is developed using a similar method, avoiding many of the di culties traditionally encountered while developing fuzzy systems (the e ectively in nite design degrees of freedom available while designing the system). Finally, the results of the two diagnostic systems are compared in terms of accuracy of fault diagnosed, accuracy of the health parameter estimates produced, (simulation) time taken to produce a correct diagnosis, and time needed for the computation: both systems correctly diagnose each component fault, the Bayesian network diagnoses faults in about half the time from the introduction of the fault, while the fuzzy system estimates the health parameters more accurately and is less computationally intensive.
Published Version
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