Andere Kunden interessierten sich auch für
![Health Monitoring of Aircraft Structures Using Piezoelectric Sensors]( "Health Monitoring of Aircraft Structures Using Piezoelectric Sensors")
Health Monitoring of Aircraft Structures Using Piezoelectric Sensors38,99 €![Aircraft Engine Failure Detection and Resumption Artifice]( "Aircraft Engine Failure Detection and Resumption Artifice")
Aircraft Engine Failure Detection and Resumption Artifice19,99 €![AIRCRAFT ICING DETECTION, IDENTIFICATION AND RECONFIGURABLE CONTROL]( "AIRCRAFT ICING DETECTION, IDENTIFICATION AND RECONFIGURABLE CONTROL")
AIRCRAFT ICING DETECTION, IDENTIFICATION AND RECONFIGURABLE CONTROL44,99 €![Aircraft Loads Assessment for Windmilling Sustained Engine Imbalance]( "Aircraft Loads Assessment for Windmilling Sustained Engine Imbalance")
Aircraft Loads Assessment for Windmilling Sustained Engine Imbalance36,99 €![POWER LEVEL MONITORING DEVICE FOR GMRT RECEIVER]( "POWER LEVEL MONITORING DEVICE FOR GMRT RECEIVER")
POWER LEVEL MONITORING DEVICE FOR GMRT RECEIVER32,99 €![Spiking Neural Network-based Wind Speed Forecasting Design & Implement]( "Spiking Neural Network-based Wind Speed Forecasting Design & Implement")
Spiking Neural Network-based Wind Speed Forecasting Design & Implement36,99 €![Reconfigurable Flight Control System for Fighter Aircraft Using MPC]( "Reconfigurable Flight Control System for Fighter Aircraft Using MPC")
Reconfigurable Flight Control System for Fighter Aircraft Using MPC51,99 €
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 faultsis 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.