Abstract: with regard to the multiple parameters in multiple flight stages and the difficulty of accurately predicting lubricating oil consumption of civil aviation engines, a model based on neighborhood rough set (NRS) and convolutional neural network (CNN) is proposed. First, the NRS method is used to extract the flight phases that are more important to the oil consumption as feature parameters; second, the CNN is used to conduct an in-depth feature study with reference to flight phase parameters so as to make oil consumption prediction. The results show that the CNN can well complete the feature extraction of multiple oil parameters. The average absolute error between the prediction result and the actual value is 0.129 伊10 -3 m3 , and the average relative error is 3.8% , which can meet the needs of practical engineering applications and provide reference for evaluating the health status of civil aviation engine lubricating oil system.

Key words: Oil consumption, multi-parameter prediction, neighborhood rough set (NRS), convolutional neural network(CNN)