Abstract: To address the challenges posed by complex operating conditions, limited training samples, data imbalance, and the inability of conventional deep learning models to meet the requirements of ignition prediction for aero-engine combustion chambers, a network model integrating residual connections and a self-attention mechanism, called the attention residual visual geometry group network(AttResVGG), was proposed. The model used a multi-head self-attention mechanism to capture dependencies among operating condition parameters and establish a mapping between these parameters and ignition status. To address insufficient data size and class imbalance, a physically constrained data augmentation strategy was designed to synthesize new operating condition samples while maintaining key physical parameter relationships, such as the fuel-air ratio and temperature-pressure ratio.In addition, an automated machine learning algorithm based on Bayesian optimization was designed to optimize model hyperparameters, further enhancing the model's predictive performance. To validate the effectiveness of this model, experiments on two datasets show that the accuracy of the AttResVGG model reaches 97.67% and 90.48%, and the Kappa coefficients reach 0.950 5 and 0.834 6, respectively, which are better than those of the compared models.

Key words: deep learning, ignition prediction, data augmentation, self-attention mechanism, automated machine learning