Engine Health monitoring (EHM) has been very popular subject to increase aircraft availability with the minimum maintenance cost. The study is aimed at providing a method to monitor the aircraft engine health during the flight with the aim of providing an opportunity for early fault detection to improve airline maintenance effectiveness and reliability. Since the impending engine failures may cause to change the engine parameters such as Fuel Flow (FF), Exhaust Gas Temperature (EGT), engine fan speed (N1), engine compressor ...
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Engine Health monitoring (EHM) has been very popular subject to increase aircraft availability with the minimum maintenance cost. The study is aimed at providing a method to monitor the aircraft engine health during the flight with the aim of providing an opportunity for early fault detection to improve airline maintenance effectiveness and reliability. Since the impending engine failures may cause to change the engine parameters such as Fuel Flow (FF), Exhaust Gas Temperature (EGT), engine fan speed (N1), engine compressor speed (N2), etc., engine deteriorations or faults may be identified before they occur by monitoring them. So as to monitor engine health in flight, the automation of current work for EHM done manually by airlines is developed by using fuzzy logic (FL) and neural network (NN) models. FL is selected to develop automated EHM system (AEHMS), since it is very useful method for automation health monitoring. The fuzzy rule inference system for different engine faults is based on the expert knowledge and real life data in Turkish Airlines fleet. The complete loop of EHM is automatically performed by the visual basic programs and Fuzzy Logic Toolbox in MATLAB.
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