A Simulation based Intelligent Analysis Framework of Aircraft Reliability, Resilience and Vulnerability

The flight reliability has been receiving considerable attention. However, the ability of the aircraft recovers to normal flight state from a perturbation were not considered under most circumstances. In this study, a simulation based intelligent analysis framework is proposed to identify the reliability, resilience and vulnerability states of Boeing 737 MAX aircraft disturbed by Maneuvering Characteristics Augmentation System (MCAS) system abnormal activation during the flight. Multiswarm particle swarm optimization (multiswarm PSO) algorithm based test cases generation strategy, aircraft failure behavior model which reflects aerodynamics of the aircraft after the horizontal stabilizer deflection caused by MCAS abnormal activation, JSBSim and FlightGear based co-simulation with aerodynamic and visual characteristics and neural network based flight states identification method constitute the proposed framework. Study results show that the proposed method can cover the margin of resilience and vulnerability quickly and the classification model can identify aircraft flight reliability, resilience and vulnerability states corresponding to different inputs accurately. The proposed framework can be used to validate the flight reliability and system resilience in a more efficient way.