Abstract: The situational awareness loss and degradation is an important error causation in airworthiness maintenance of composite material components. The monitoring of situational awareness level has great significance to reduce maintenance errors and ensure flight safety. To monitor the situational awareness, the measurement and prediction method of situational awareness based on eye movement was proposed. Firstly, the situational awareness of composite materials bonding repair was analyzed according to the maintenance process and the theoretical model of situational awareness. Then, the situational awareness measurement experiment was designed. In this experiment, the eye movement data was recorded by Tobii and the SART was used to measure the situational awareness subjectively. Dependent on the correlation analysis between eye movement and situational awareness, the feature mode of eye movement was constructed to measure the situational awareness. Finally, the situational awareness prediction model was established by relevance vector machine with this pattern. The experimental results show that the prediction accuracy and fitting effect of the model can support the online monitoring and evaluation of the maintenance situational awareness.

Key words: composite materials, airworthiness maintenance, eye movement, situational awareness, RVM