發(fā)布時間：2018-11-08 15:57

【摘要】：由于日益增長的飛行安全和飛機(jī)維護(hù)質(zhì)量需求,飛機(jī)使用可靠性已經(jīng)成為一個重要的研究領(lǐng)域。從某航空公司波音737飛機(jī)使用過程中現(xiàn)場所記錄的18年的故障數(shù)據(jù)出發(fā),應(yīng)用奇異譜分析(SSA)方法,對故障時間序列進(jìn)行了建模和預(yù)測,進(jìn)一步以預(yù)測結(jié)果的均方根誤差(RMSE)最小為優(yōu)化目標(biāo)對SSA模型參數(shù)進(jìn)行了優(yōu)選。在此基礎(chǔ)上,提出了一種更為廣泛的模型組合方法和實現(xiàn)算法,這種方法采用不同的時間序列模型來構(gòu)造SSA分解出的趨勢、周期和殘差等成分。通過與三次指數(shù)平滑(Holt-Winters)、自回歸移動平均(ARIMA)2種時間序列模型的實驗結(jié)果對比,SSA及其參數(shù)優(yōu)選和模型組合方法在故障時間序列分析中具有更好的擬合和預(yù)測精度。
[Abstract]:Aircraft reliability has become an important research field due to the increasing requirements of flight safety and aircraft maintenance quality. Based on the 18 year fault data recorded during the operation of a Boeing 737 aircraft, the fault time series was modeled and predicted by using singular spectrum analysis (SSA) method. Furthermore, the minimum root mean square error (RMSE) of the prediction results is taken as the optimization objective to optimize the parameters of the SSA model. On this basis, a more extensive model combination method and implementation algorithm are proposed. Different time series models are used to construct the trend, period and residual components of SSA decomposition. By comparing the experimental results with the two time series models of cubic exponential smoothing (Holt-Winters) and autoregressive moving average (ARIMA), SSA and its parameter optimization and model combination methods have better fitting and prediction accuracy in fault time series analysis.
【作者單位】： 北京航空航天大學(xué)計算機(jī)學(xué)院;中航工業(yè)西安航空計算技術(shù)研究所;
【基金】：中國民用航空專項研究項目(MJ-S-2013-10)~~
【分類號】：V267

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