基于时序分解的飞机平均故障间隔飞行时间组合预测
|
摘要
针对飞机平均故障间隔飞行时间(MFHBF)指标值的预测问题,提出一种基于时间序列分解的组合预测方法。首先利用STL(Seasonal and Trend Decomposition using Loess)方法分解出MFHBF指标的长期趋势项和季节变动项,然后以灰色模型预测指标的长期趋势,以BP神经网络和支持向量机回归组合模型分别预测指标的季节变动,根据误差权重计算季节变动的加权值,最后以加法模型合并趋势和季节的预测值获得最终结果。利用服务点积累的指标数据对方法进行检验,与单独使用支持向量机回归预测得到的结果相比,平均绝对误差由45%减小至21%,证明该方法能够有效提高预测精度,为保障人员提供可信的指标预测结果。
Concerning the prediction accuracy of aircraft' s Mean Flying Hours between Failures( MFHBF) index,a combination forecasting method based on time series decomposition was proposed.Firstly,the MFHBF data was decomposed into the long term trend and seasonal variation by using the Seasonal and Trend Decomposition using Loess( STL).Secondly,the long-term trend of the index was predicted by the grey model,and the seasonal variation was separately predicted according to Back Propagation( BP) neural network and support vector machine regression model.The weighted value of seasonal variation was calculated according to error weight.Finally,the final result was calculated by combining the seasonal and trend prediction values under the addition model.The practical prediction effect of the method was verified by the measured data from the service point.The mean absolute error was reduced to 21% by using this method in contrast to 45% by using support vector machine regression model alone.This method can effectively improve the prediction accuracy,and provide credible index prediction results for security personnel.
Concerning the prediction accuracy of aircraft' s Mean Flying Hours between Failures( MFHBF) index,a combination forecasting method based on time series decomposition was proposed.Firstly,the MFHBF data was decomposed into the long term trend and seasonal variation by using the Seasonal and Trend Decomposition using Loess( STL).Secondly,the long-term trend of the index was predicted by the grey model,and the seasonal variation was separately predicted according to Back Propagation( BP) neural network and support vector machine regression model.The weighted value of seasonal variation was calculated according to error weight.Finally,the final result was calculated by combining the seasonal and trend prediction values under the addition model.The practical prediction effect of the method was verified by the measured data from the service point.The mean absolute error was reduced to 21% by using this method in contrast to 45% by using support vector machine regression model alone.This method can effectively improve the prediction accuracy,and provide credible index prediction results for security personnel.
引文
[1]李瑞莹,康锐.基于ARMA模型的故障率预测方法研究[J].系统工程与电子技术,2008,30(8):1588-1591.
[2]刘东君,邹志红.灰色和神经网络组合模型在水质预测中的应用[J].系统工程,2011,29(9):105-109.
[3]赵建忠,徐廷学,葛先军,等.基于小波变换和GM-ARMA的导弹备件消耗预测[J].北京航空航天大学学报,2013,39(4):553-558.
[4]熊高峰,韩鹏,聂坤凯.时间序列分解在短期电价分析与预测中的应用[J].电力系统及其自动化学报,2011,23(3):95-100.
[5]汪同三,张涛.组合预测——理论、方法及应用[M].北京:社会科学文献出版社,2008.
[6]乔占俊.基于Census X12-SARIMA模型的中长期负荷预测[J].电力系统及其自动化学报,2014,26(1):34-38.
[7]梁中耀,刘永,盛虎,等.滇池水质时间序列变化趋势识别及特征分析[J].环境科学学报,2014,34(3):754-762.
[8]曾波,刘思峰,方志耕,等.灰色组合预测模型及其应用[J].中国管理科学,2009,17(5):150-155.
[9]温胜强,周鹏飞,康海贵.基于灰色理论与BP神经网络的交通运输量组合预测研究[J].大连理工大学学报,2010,50(4):547-550.
[10]KUNWAR P S,ANKITA B,AMRITA M,et al.Artificial neural network modeling of the river water quality—a case study[J].Ecological Modelling,2009,220(6):888-895.
[11]闻敬谦,李青,赵庆斌.基于神经网络的维修保障流程影响度分析方法[J].北京航空航天大学学报,2011,37(6):690-694.
[12]傅贵,韩国强,逯峰,等.基于支持向量机回归的短时交通流预测模型[J].华南理工大学学报(自然科学版),2013,41(9):71-76.
[13]周国雄,莫晓山.基于灰色预测和BP的集气管压力集成预测方法[J].仪器仪表学报,2011,32(7):1648-1654.
[14]刘海燕,蒋泽军.基于时间序列分析和ACO-LSSVM的故障预测技术研究[J].计算机与现代化,2013(5):219-226.
[2]刘东君,邹志红.灰色和神经网络组合模型在水质预测中的应用[J].系统工程,2011,29(9):105-109.
[3]赵建忠,徐廷学,葛先军,等.基于小波变换和GM-ARMA的导弹备件消耗预测[J].北京航空航天大学学报,2013,39(4):553-558.
[4]熊高峰,韩鹏,聂坤凯.时间序列分解在短期电价分析与预测中的应用[J].电力系统及其自动化学报,2011,23(3):95-100.
[5]汪同三,张涛.组合预测——理论、方法及应用[M].北京:社会科学文献出版社,2008.
[6]乔占俊.基于Census X12-SARIMA模型的中长期负荷预测[J].电力系统及其自动化学报,2014,26(1):34-38.
[7]梁中耀,刘永,盛虎,等.滇池水质时间序列变化趋势识别及特征分析[J].环境科学学报,2014,34(3):754-762.
[8]曾波,刘思峰,方志耕,等.灰色组合预测模型及其应用[J].中国管理科学,2009,17(5):150-155.
[9]温胜强,周鹏飞,康海贵.基于灰色理论与BP神经网络的交通运输量组合预测研究[J].大连理工大学学报,2010,50(4):547-550.
[10]KUNWAR P S,ANKITA B,AMRITA M,et al.Artificial neural network modeling of the river water quality—a case study[J].Ecological Modelling,2009,220(6):888-895.
[11]闻敬谦,李青,赵庆斌.基于神经网络的维修保障流程影响度分析方法[J].北京航空航天大学学报,2011,37(6):690-694.
[12]傅贵,韩国强,逯峰,等.基于支持向量机回归的短时交通流预测模型[J].华南理工大学学报(自然科学版),2013,41(9):71-76.
[13]周国雄,莫晓山.基于灰色预测和BP的集气管压力集成预测方法[J].仪器仪表学报,2011,32(7):1648-1654.
[14]刘海燕,蒋泽军.基于时间序列分析和ACO-LSSVM的故障预测技术研究[J].计算机与现代化,2013(5):219-226.