Aircraft Engine Fuel Flow Parameter Prediction and Health Monitoring System

In Aircraft engines, condition monitoring based strategies are used to lessen upkeep costs, ensure aircraft wellbeing and to reduce the fuel utilization. Currently the performance deterioration of aircraft engines is determined using parameters such as fuel flow, engine fan speed, vibration, oil weight, oil temperature and Exhaust Gas Temperature (EGT) etc. In this paper, a model has been proposed to obtain the performance deterioration of Turboprop engine. In this paper, Multiple Regression Analysis (MRA) with Artificial Neural Network (ANN) and Data clustering with fuzzy logic approach is performed for the prediction of Fuel Flow (FF) parameter and compared for accuracy of their prediction with minimum performance error. Using this model, any performance deterioration that may happen in the aircraft turboprop engine can be effectively recognized and this could also be a marker for the pilots in case of the occurrence of fault in the fuel flow parameter sensor.