奇异谱分析在故障时间序列分析中的应用

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。 

Application of singular spectrum analysis to failure time series analysis

Due to significant industrial demands toward flight safety andairplane maintenance quality, improving airplane's reliability in usage stage has become an important activity and the research domain is rapidly evolving. In this paper, eighteen years' field data gathered from the maintenance phase of two Boeing 737 aircrafts are prepared as time-to-failure series. Then singular spectrum analysis (SSA) is usedto cope with this data for modeling and forecasting. Furthermore, a SSA parameter optimization algorithm is proposed by minimizing root mean square error (RMSE) of the prediction results. Based on this,a broader method of model combination is raised by utilizing different time series models to the components obtained from SSA decomposition, which represent trend, period, residuals, etc.The combination model and detailed algorithm are designed. The experimental results are compared with those of cubic exponential smoothing (Holt-Winters) and autoregressive integrated moving average (ARIMA), which verifies that the proposed models and algorithms have better fitting and prediction accuracyin failure time series analysis.