Development of a Fuzzy System for Jet Engine Diagnostics and Comparison with a Bayesian Diagnostic System

摘要

Current diagnostics on most gas turbine engines involve offline processing only. Since failures can cause serious safety and efficiency 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 difficulties traditionally encountered while developing fuzzy systems (the effectively infinite 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.