| Citation: | YAO Shangpeng, WANG Yankai, LUO Xiao. Prediction of full-condition vibration trend of aero-engine based on combined model[J]. Journal of Aerospace Power, 2025, 40（7）：20240209 doi: 10.13224/j.cnki.jasp.20240209
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The steady state and transition state are intertwined during the operation of aero-engine, resulting in strong nonlinearity and high time variability of vibration trend data. In view of the problems of single working condition and low prediction accuracy in the existing vibration trend prediction research, a combined model was proposed to predict the vibration trend of an aero-engine based on the full-condition. The improved variational modal decomposition algorithm (IVMD) was used to decompose the vibration data to weaken the nonlinearity and time-varying nature; the improved sparrow search algorithm (ISSA) was used to optimize the network parameters of the IVMD-BiLSTM (bidirectional long and short-term memory) prediction model; and the prediction performance of the prediction model was examined based on the vibration trend data of the aircraft engine under the steady state and transition state conditions. Finally, the prediction performance of the model was examined under steady state and transitional conditions based on aero-engine vibration trend data. The model validation results showed that: for the vibration trend data in full-conditions, after data decomposition and optimization of the combined model parameters by the ISSA, the comprehensive evaluation indicators (CEI) of the prediction effect can be up to 55.13%; besides, the combined model had excellent prediction performance for the vibration trend of steady state and transition state in different states, and the model had good generalization.
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