As the core component of an aircraft, an aero-engine provides power to the aircraft and its reliability is the key to ensure the safety of the aircraft. The remaining useful life prediction is of great significance to improve the availability of aero engines and reduce their maintenance costs. To achieve end-to-end RUL prediction and improve the prediction accuracy, this paper proposes an aero-engine remaining life prediction method based on the MPFAM-FCN model, and experiments are conducted on the dataset of C-MAPSS to verify the effectiveness of the proposed method. First, the C-MAPSS dataset is preprocessed; then the training set is input to the MPFAM-FCN prediction model for training; finally, the validation comparison is carried out based on the C-MAPSS dataset. The results show that the model can accurately predict the remaining service life of an aero-engine, and its prediction is also better than some existing advanced algorithms.
Remaining useful life estimation of aeroengine based on MPFAM-FCN
16.06.2023
1242601 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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