基于内嵌物理约束神经网络模型的航空发动机数字工程模型

A digital model-based prognostics and health management（PHM）system is crucial for digitali-zation，intelligence in aeroengine. Among all digital models，an aeroengine performance digital model is one of the basic modules for PHM system，which is used for condition monitoring and performance prediction on aeroengine. In this work，a strategy for creating a performance digital model to predict aeroengine thrust is given. The strategy is to combine aeroengine domain knowledge and artificial neural networks，which is to create an architecture for tailoring the neural network model with physical information. More，the given model is designed to address feature selection. The application of the given model to aeroengine thrust prediction demonstrates its effectiveness in accuracy with the different testing datasets. Compared with the conventional neural network，the average relative error of the architecture-based model is small，and the max relative error of the architecture-based model is only 1/4 of it under the same model size. With physical constraint，the model is less reliant on training data，and the number of layers and the hyperparameters in the neural networks model are intervened.