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.