303 / 2025-06-19 19:28:42

CNN-BisLSTM-Attention: A Hybrid Prediction Model for Key Parameters of Aero-Engines

aero-engine,prediction,CNN,LSTM,multi-head self-attention mechanism

This paper presents CNN-BisLSTM-Attention, a hybrid model for predicting key parameters of aero-engines. The architecture integrates two core modules: (1) A spatio-temporal feature extraction module combining convolutional neural networks for local spatial patterns and bidirectional stabilized long short-term memory (BisLSTM) for long-term dependencies; (2) A self-attention feature enhancement module introducing a multi-head self-attention mechanism to optimize discriminative feature weights. Real data from aero-engines are applied to velidate the performance of the model. Experimental results manifest that the CNN-BisLSTM-Attention model outperforms the comparison models in prediction accuracy. Specifically, for MAE and RMSE indicators, the proposed model improved by 18.6%-46.0% and 14.4%-43.5%, respectively. Additionally, ablation studies prove the contributions of each component of the proposed hybrid prediction model.