| Summary: | Engineering systems typically go through complicated degradation processes, partially due to their multiple operating modes. Therefore, how to accurately estimate their remaining useful life is a critical issue. To address this challenge, a hybrid degradation modeling and prognostic method for the multi-modal system is proposed. Firstly, the cumulative dynamic differential health indicator is constructed for the multi-modal switching system using a multi-objective optimization approach. The long-term cumulative degradation assessment model is constructed based on the gated recurrent unit. Then, considering that the damage in the latest stage has a significant impact on the remaining useful life, the time window is used to extract local features of the sequence, including energy features and statistical features. The latest stage degradation is predicted based on the light gradient boosting machine. Finally, model averaging is used to integrate the two predicted results, which is expected to improve the prognostic robustness. The proposed model is evaluated with synthetic analysis and NASA turbofan aero-engine datasets. Extensive experimental results demonstrate the proposed method provides a better characterization of the degradation status of the system and provides a higher estimation accuracy than existing methods.
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