摘要
实时预测民航发动机滑油量对保障飞行安全具有重要意义。针对滑油量受发动机多个工作状态的多个参数影响,具有影响参数多,提取方法不确定等问题,提出了一种基于邻域粗糙集(neighborhood rough set,NRS)和灰狼优化(grey wolf optimizer,GWO)-Elman相结合的方法预测滑油量。首先通过邻域粗糙集提取对滑油量重要度高的发动机工作阶段,将提取后的工作阶段有关参数作为特征向量输入到灰狼优化-Elman的网络模型中,灰狼算法通过计算和比较个体的适应度来优化Elman网络中的权值和阈值,保证Elman网络中的权值和阈值达到全局最优。预测结果表明,精度达到98.44%,满足工程应用的精度要求。研究结果为及时监测民航发动机滑油系统的健康状况提供理论依据。
It is very important to predict the oil quantity of civil aviation engine in real time to ensure flight safety.As the oil quantity is affected by multiple parameters of the engine in multiple working states,it has the problems of many influencing parameters and uncertain extraction method.Therefore,a method based on the combination of neighborhood rough set(NRS)and grey wolf optimization(GWO)-Elman was proposed to predict the oil quantity.Firstly,the engine working stage with high importance to the oil quantity was extracted by neighborhood rough set,and the relevant parameters of the extracted working stage were input into the Elman network model of grey wolf optimization as feature vectors.The gray wolf algorithm optimized the weights and thresholds in Elman network by calculating and comparing individual fitness,so the algorithm can ensure that the weights and thresholds in Elman network reach the global optimization.The prediction results show that the accuracy reaches 98.44%,which meets the requirements of engineering application.This study provides a theoretical basis for timely monitoring the lubricating oil system health status of civil aviation engines.
作者
瞿红春
高鹏宇
朱伟华
许旺山
郭龙飞
QU Hong-chun;GAO Peng-yu;ZHU Wei-hua;XU Wang-shan;GUO Long-fei(College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300,China)
出处
《科学技术与工程》
北大核心
2021年第14期6069-6074,共6页
Science Technology and Engineering
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(ZXH2010D019)。
关键词
滑油量预测
特征参数提取方法
灰狼优化
ELMAN神经网络
oil quantity prediction
feature parameter extraction method
grey wolf optimizer
Elman neural network
