The Parameter Augmentation Pretraining Neural Network Model of Aero-Engine Thrust Prediction

Accurately predicting the thrust of aero-engine is important for engine health management and engine performance control. Thrust prediction neural network models have been widely applied in recent years. This paper proposes a parameter augmentation pretraining method and designs an aero-engine neural network model with physical significance. It is known that ground test system measures more engine parameters compared to onboard system. Due to the limitations of using onboard models, typically only the parameters measured by both systems are used as training parameters, which may lead to incomplete training. In this study, a 0-1 encoded vector is utilized to distinguish ground test system data and onboard system data. The model is first pretrained using the measurable parameters from the ground test system, following by retraining using parameters measured by both the ground test system and the onboard system. The parameter augmentation pretrain method allows the model to learn more mapping relationships of aero-engine and resolves the issue of parameter dimension matching. Furthermore, this study investigates the impact of the weight loss function on the prediction accuracy of the model. This research provides guidance for engine performance evaluation and further studies.