基于NRS-CNN的民航发动机滑油消耗量预测  

Prediction of lubricating oil consumption of civil aviation engine based on NRS-CNN

作　　者：瞿红春[1] 高鹏宇 朱伟华 许旺山 郭龙飞 QU Hongchun;GAO Pengyu;ZHU Weihua;XU Wangshan;GUO Longfei(College of Aeronautical Engineering,CAUC,Tianjin300300,China)

机构地区：[1]中国民航大学航空工程学院,天津300300

出　　处：《中国民航大学学报》2021年第5期16-21,27,共7页Journal of Civil Aviation University of China

基　　金：中国民航大学科研基金项目(05yk08m);中央高校基本科研业务费专项(ZXH2010D019)。

摘　　要：针对民航发动机滑油消耗量受多个飞行阶段的多个参数影响而难以准确预测的问题,提出基于邻域粗糙集(NRS,neighborhood rough set)和卷积神经网络(CNN,convolutional neural network)的模型来预测滑油消耗量。首先,采用NRS方法提取对滑油消耗重要度较高的飞行阶段状态参数作为特征参数;然后,利用CNN对重要度高的飞行阶段状态参数进行深度特征学习,实现滑油消耗量的预测。预测结果表明:CNN能很好地完成对多滑油参数的特征提取,预测结果与实际值的平均绝对误差为0.129×10^(-3)m^(3),平均相对误差为3.8%,可满足实际工程应用的需要,为评估民航发动机滑油系统的健康状况提供参考。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 convolu-tional neural network(CNN)is proposed.First,NRS method is used to extract the flight phases state parameters which 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 predict oil consumption.The results show that CNN can well complete the feature extraction of multiple oil parameters.The average absolute error between the prediction result and the actual value is 0.129×10-3 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 lubricating oil system of civil aviation engine.

关 键 词：滑油消耗量 多参数预测 邻域粗糙集 卷积神经网络 

分 类 号：V233.4[航空宇航科学与技术—航空宇航推进理论与工程]