基于遗传算法和ARMA模型的航空发电机寿命预测  

Life Prognostics for Aero-generator Based on Genetic Algorithm and ARMA Model

作　　者：崔建国[1] 赵云龙[1] 董世良[2] 张红梅[1] 陈希成[2] 

机构地区：[1]沈阳航空航天大学自动化学院,辽宁沈阳110136 [2]沈阳飞机设计研究所,辽宁沈阳110035

出　　处：《航空学报》2011年第8期1506-1511,共6页Acta Aeronautica et Astronautica Sinica

基　　金：航空科学基金(2010ZD54012);辽宁省教育厅科研基金(2008544);国防基础科研计划项目(A0520110023)~~

摘　　要：针对航空发电机剩余使用寿命难以准确预测的问题,提出一种基于遗传算法(GA)优化的自回归与移动平均(ARMA)模型。运用航空发电机寿命专用试验平台,对某型航空发电机寿命进行长期试验,获取该航空发电机寿命相关数据。深入分析寿命试验数据,并对其建立ARMA模型。在此基础上,采用遗传算法对ARMA模型的阶数进行优化,分别采用ARMA模型与经遗传算法优化后的ARMA模型对航空发电机的使用寿命进行预测研究。结果表明,优化前后两种ARMA模型均可对航空发电机的使用寿命实现预测效能。优化前ARMA模型对航空发电机寿命预测的平均相对误差为4.33%;而经遗传算法优化后,ARMA模型预测的平均相对误差仅为2.26%,能更准确预测航空发电机的使用寿命,具有很好的工程应用价值。To improve the aero-generator life prediction accuracy,an optimized auto-regressive and moving average（ARMA） model based on the genetic algorithm（GA） is presented.A specific experimental platform is used to perform long-term life prediction experiments on a certain type of aero-generator and collect the related test data.After a thorough analysis of these test data,a corresponding ARMA model is designed,and the genetic algorithm is used to carry on the exponent number optimization of the model.Then the original and the optimized ARMA models are used respectively to conduct life prediction research on the service life of the aero-generator.The result shows that these two models can realize the function of predicting the service life of an aero-generator.The average relative prognostic error of the ARMA model after the optimization is 2.26%,which is less than 4.33%,the error of the original model without optimization.Thus a conclusion can be drawn that the optimized ARMA model can predict the service life of an aero-generator more accurately and this model may find wide application in engineering practice.

关 键 词：遗传算法 ARMA模型 航空发电机 寿命预测 注油压力 

分 类 号：V242.4[航空宇航科学与技术—飞行器设计] TP206.3[自动化与计算机技术—检测技术与自动化装置]