航空发动机滑油消耗率计算与预测方法
A Calculation and Prediction Method of Lubricating Oil Consumption Rates for Aeroengines
张振生 1蔡景 1张瑞 1张航源1
作者信息
- 1. 南京航空航天大学民航学院,南京 211106
- 折叠
摘要
针对航空发动机滑油箱油量测量值易受多个参数影响导致滑油消耗率难以计算和预测的问题,提出了一种改进的滑油量数据提取规则和滑油消耗率预测方法.基于密度聚类算法(Density-based spatial clustering of applications with noise,DBSCAN)等方法对发动机数据进行了清洗,获取平稳飞行状态下滑油量数据.使用最小二乘法对滑油量进行拟合,得到了滑油消耗率,平均拟合优度达到了 0.86.在此基础上,利用多层感知器(Multi-layer perception,MLP)建立了滑油消耗率与飞行状态参数之间的关系,预测结果与实际值的平均绝对百分比误差为1.15%.本文提出的方法能够满足实际工程需求,为评估航空发动机滑油系统的健康状况提供了可靠参考.
Abstract
Since the measurement of oil quantity in aviation engine lubrication systems is susceptible to multiple parameters,it is difficult to calculate and predict oil consumption rates.We propose an improved method for extracting oil quantity data and predicting oil consumption rates.Engine data are cleansed using density-based clustering algorithms,including density-based spatial clustering of applications with noise(DBSCAN),to obtain stable oil quantity data during steady flight conditions.By the least squares method,oil consumption rates are derived with an average fitting goodness of 0.86.Subsequently,a multi-layer perception(MLP)is employed to establish the relationship between oil consumption rates and flight status parameters,resulting in a predicted average absolute percentage error of 1.15%,compared to actual values.The proposed method meets practical engineering requirements,providing a reliable reference for assessing the health status of aviation engine lubrication systems.
关键词
航空发动机/滑油消耗率/基于密度聚类算法/多层感知器Key words
aeroengine/oil consumption rate/density-based spatial clustering of applications with noise(DBSCAN)/multi-layer perception(MLP)引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据