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Previous research at WVU has yielded promising results in the detection of aircraft sub-systems malfunctions using the artificial immune system (AIS) paradigm. However, one aircraft component that requires improvement is the aircraft propulsion system. In this research effort, MAPSS, a non-real time low-bypass turbofan engine model distributed by NASA, has been linearized and interfaced with the WVU F-15 model and the WVU 6 degrees-of-freedom flight simulator to provide a more complex engine model and create more options for engine failure modeling and engine failure detection. A variety of engine actuator and sensor failures were modeled and implemented into the simulation environment. A detection scheme based on the AIS approach was developed for specific classes of failures including throttle, burner fuel flow valve, variable nozzle area actuator, variable mixer area actuator, low-pressure spool speed sensor, low-pressure turbine exit static pressure sensor, and mixer pressure ratio sensor.A 5-dimensional feature hyper-space is determined to build the “self” within the AIS paradigm for abnormal condition detection purposes. The WVU AIS interactive design environment based on evolutionary algorithms was used for data processing, detector generation, and limited optimization. Flight simulation data for system development and testing was acquired through experiments in the WVU 6 degrees-of-freedom flight simulator over extended areas of the flight envelope. The AIS-based detection scheme was tested using both nominal and engine failure conditions and its performance evaluated in terms of detection rates and false alarms. As compared to the previous failure detection results, significant improvement has been demonstrated as well as excellent potential for detection of the newly modeled engine failures.

Modeling of jet engine abnormal conditions and detection using the artificial immune system paradigm