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In this study the design of motion‐based flight simulators is carried out by specifying the performance required of the motion cueing mechanism, to generate translational and angular motions as a 6–3 Stewart Platform Mechanism (SPM). These motions are intended to approximate the specific forces and angular accelerations encountered by the pilot in the simulated aircraft. Firstly, the dynamics of this 6–3 SPM is given in closed form as in our earlier study. Then, for the control of obtained dynamic model, a leg‐length based PD algorithm is applied. In the optimization of the applied PD algorithm's coefficients, Real Coded Genetic Algorithms are used. So as to have faster and effective system's performance, the fitness function chosen, in Genetic Algorithms, having maximum overshoot value, settling time and steady state error which are obtained from the unit step response. The performance of the system studied is compared to the similar studies in the literature exist.

The purpose of this paper is to develop an automated engine health monitoring system (AEHMS) for commercial aircraft.

The AEHMS is developed by using fuzzy logic. The input of the fuzzy logic is engine performance parameters gathered from aircraft for every flight during cruise. The fuzzy rule inference system for different engine faults is based on expert knowledge and real life data in the Turkish Airlines fleet. The very smallest is used for defuzzification, since it provides a more meaningful result than others. The complete loop of engine health monitoring (EHM) is automatically performed by the programs and Fuzzy Logic Toolbox in MATLAB. The system produces output values between 0 – faulty and 1 – not faulty for every fault or deterioration on a time series. The program triggers an alert if any output exceeds a specified value. Finally, the method is utilized for monitoring the engines in the Turkish Airlines fleet.

Health monitoring has been a very popular subject to increase aircraft availability with the minimum maintenance cost. Fuzzy logic is a very useful method for automated health monitoring strategies.

It does not provide long‐term engine maintenance decisions such as scheduling overhaul times, predicting the remaining life of the engine components.

The paper provides a robust method for EHM with the application to real aircraft data. The AEHMS can greatly simplify the EHM system for airlines and minimizes its drawbacks, such as extra labor hours, human error and requirement for engineering expertise.