Remaining useful life probabilistic prognostics using a novel dual adaptive sliding-window hybrid strategy

The reliable, rapid, and accurate Remaining Useful Life (RUL) prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs. To achieve the reliable, rapid, and accurate RUL prognostics, the balance between accuracy and computational burden deserves more attention. In addition, the uncertainty is intrinsically present in RUL prognostic process. Due to the limitation of the uncertainty quantification, the point-wise prognostics strategy is not trustworthy. A Dual Adaptive Sliding-window Hybrid (DASH) RUL probabilistic prognostics strategy is proposed to tackle these deficiencies. The DASH strategy contains two adaptive mechanisms, the adaptive Long Short-Term Memory-Polynomial Regression (LSTM-PR) hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation (KDE) probabilistic prognostics mechanism. Owing to the dual adaptive mechanisms, the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics. Based on the degradation dataset of aircraft electromagnetic contactors, the superiority of DASH strategy is validated. In terms of probabilistic, point-wise and integrated prognostics performance, the proposed strategy increases by 66.89%, 81.73% and 25.84% on average compared with the baseline methods and their variants.