Aircraft Failure Detection and Identification over an Extended Flight Envelope Using an Artificial Immune System

An integrated artificial immune system-based scheme that can operate over extended areas of the flight envelope is proposed in this paper for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. A hierarchical multi-self strategy has been developed in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the scheme. The aircraft model represents a supersonic fighter, including model-following direct adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all the categories of failures considered.