A Hybrid Model Based on Singular Spectrum Analysis and Support Vector Machines Regression for Failure Time Series Prediction

Effectively forecasting the failure data in the maintenance stage is essential in many reliability planning and scheduling activities. Although a number of data-driven techniques have been applied to cope with this issue and achieved noteworthy performance, the reliability prediction problem is still not fully explored, especially for applying the hybridization methods. In this paper, we introduce a hybrid model which integrates singular spectrum analysis (SSA) and support vector machines regression (SVR) to forecast the failure time series data gathered from the maintenance stage of the Boeing 737 aircraft. Two significant components recognized as the trend and fluctuation are extracted from the original failure time series data by using the techniques of SSA and noise test, and the two components are associated with the inherent and operational reliability, respectively. Then two models named as trend-SSA and fluctuation-SVR are individually developed to conduct the tasks of modeling and forecasting the two components. Furthermore, the optimal parameters of the hybrid model are obtained efficiently by a stepwise grid search method. The performance of the presented model is measured against other unitary models such as Holt-Winters, autoregressive integrated moving average, multiple linear regression, group method of data handling, SSA, and SVR, as well as their hybridizations. The comparison results indicate that the proposed model outperforms other techniques and can be utilized as a promising tool for reliability forecast applications.