Fuzzy logic-based automated engine health monitoring for commercial aircraft

Purpose - The purpose of this paper is to develop an automated engine health monitoring system (AEHMS) for commercial aircraft. Design/methodology/approach - 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. Findings - 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. Research limitations/implications - It does not provide long-term engine maintenance decisions such as scheduling overhaul times, predicting the remaining life of the engine components. Originality/value - 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.