Fault Diagnosis of RM12 based on identified models

The jetengines of today are growing in complexity. Reliability for aircraft engines are of extreme importance, mainly due to safety reasons but also economical ones. This master thesis deals with faultdiagnosis in the turbine section of RM12, the engine used in Saab/BAe's Gripen. Three different faults which can occur in the turbine section was studied. These faults are: clogged fuel nozzle, hole in outlet guide vane and sensor fault. An analysis of the behaviour of the engine with these faults present was made. Based on this analysis an existing simulation model of RM12 was modified, so that these faults could be simulated. For the purpose of fault diagnosis two models were developed for two different engine parameters, one linear state space model and a neural network. These two models are then used to isolate the faults. The linear state space model is used to estimate the temperature right behind the engine turbines. This is a state space model with two states. This model estimates the temperature well at higher throttle levels, but has a temperature discrepancy of almost 100 K at lower throttle levels, the temperature right behind the turbines varies between 300 and 1200 K. A neural network was estimated to detect a decrease in turbine efficiency which is a phenomena which occurs when one or several of the engine's eighteen fuel nozzles are clogged. The neural network was able to detect this fault at some points. The diagnosis algorithm developed, based on the models mentioned above, is able to detect faults at most operating points, but fails to isolate the present fault at some points