Fuzzy Logic Diagnostic for Turbine Engine Health Condition

Abstract

This paper investigates the turbine engine health condition by using fuzzy logic modeling technology in Flight Data Recorder (FDR) data of the aircraft. The turbine engine health monitoring and diagnosis is to use the information of onboard flight data recorders to monitoring the engines motion with fuzzy logic and computational numeric analysis to do the health diagnosis and monitoring of the aircraft’s engines. Analysis techniques for aircraft engine health monitoring and diagnosis were included exceed analysis, statistical analysis and validated trend information. The diagnostic algorithm is designed to track engine health degradation by using those data analyses. It has the capability to provide the causes of flight events in details and to detect the potential problems of engine health through the monitoring management. This model-based effectiveness to detect well for the five sets of aircraft flight data, involving a four-engine passenger jet transport. Because of those four engine models are generated to predict the performance by the exhaust gas temperature excursions along the flight trajectory. Excursion is assumed to have occurred if the indicated exhaust gas temperature exceeds the model prediction.