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.

This paper proposes a novel infrared-inertial navigation method for the precise landing of commercial aircraft in low visibility and Global Position System (GPS)-denied environments. Within a Square-root Unscented Kalman Filter (SR_UKF), inertial measurement unit (IMU) data, forward-looking infrared (FLIR) images and airport geo-information are integrated to estimate the position, velocity and attitude of the aircraft during landing. Homography between the synthetic image and the real image which implicates the camera pose deviations is created as vision measurement. To accurately extract real runway features, the current results of runway detection are used as the prior knowledge for the next frame detection. To avoid possible homography decomposition solutions, it is directly converted to a vector and fed to the SR_UKF. Moreover, the proposed navigation system is proven to be observable by nonlinear observability analysis. Last but not least, a general aircraft was elaborately equipped with vision and inertial sensors to collect flight data for algorithm verification. The experimental results have demonstrated that the proposed method could be used for the precise landing of commercial aircraft in low visibility and GPS-denied environments.

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