论文部分内容阅读
由于日益增长的飞行安全和飞机维护质量需求,飞机使用可靠性已经成为一个重要的研究领域。从某航空公司波音737飞机使用过程中现场所记录的18年的故障数据出发,应用奇异谱分析(SSA)方法,对故障时间序列进行了建模和预测,进一步以预测结果的均方根误差(RMSE)最小为优化目标对SSA模型参数进行了优选。在此基础上,提出了一种更为广泛的模型组合方法和实现算法,这种方法采用不同的时间序列模型来构造SSA分解出的趋势、周期和残差等成分。通过与三次指数平滑(Holt-Winters)、自回归移动平均(ARIMA)2种时间序列模型的实验结果对比,SSA及其参数优选和模型组合方法在故障时间序列分析中具有更好的拟合和预测精度。
The reliability of aircraft use has become an important area of research due to increasing flight safety and aircraft maintenance quality requirements. Based on 18-year fault data recorded at the scene of an airline’s Boeing 737 aircraft, a singular spectrum analysis (SSA) method was used to model and predict the fault time series. The root mean square error (RMSE) minimum for the optimization of SSA model parameters are preferred. On this basis, a more extensive model combination method and its implementation algorithm are proposed. This method uses different time series models to construct the components, such as trends, periods and residuals, decomposed by SSA. Compared with experimental results of two time series models of autoregressive moving average (ARIMA) and three-time exponential smoothing (Holt-Winters), the SSA and its parameter optimization and model combination method have better fitting in fault time series analysis Prediction accuracy.