| Current diagnostics on most gas turbine engines involve off-line 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. This project applies some of the methodology of Rausch, et al. to a simulation of a low bypass turbofan. The model uses 9 health parameters to simulate faults or degradation of engine components. Sensor residuals from an extended Kalman filter were used with a non-linear engine model to estimate the engine health parameters. Other methods for generating health parameter estimates were also implemented and compared, including a tracking filter based on Newton's method and a back-propagation neural network. An implementation of a Bayesian network to engine fault diagnostics is demonstrated and 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 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. The Bayesian network and fuzzy system have the best overall performance: both systems correctly diagnose each component fault, while the LKF and tracking filter fail for some cases and the neural network fails under some conditions. 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. |
