A novel method for aeroengine performance model reconstruction based on CDAE model

Aeroengine is a complex multi-module system. Due to the limitation of sensor cost and sensor installation conditions, it is usually impossible to install a large number of sensors to measure the physical parameters of the aeroengine modules to establish the accurate module characteristic models to achieve the purpose of module performance evaluation. To address this issue, the high-dimensional physical field reconstruction strategy base on limited measurement data is developed, which is of great significance to the modeling of module characteristics. A reconstruction framework of a high-dimensional physical field based on limited measurement data is built. The mapping relationship between limited measurement data and high-dimensional physical field data is established, and the relevant learning strategies based on the deep learning network are designed. To verify the effectiveness of the proposed method, the simulation dataset generated by the multi-component closed-loop simulation system and the aeroengine service dataset are used for experimental verification, and the mean and variance of mean square error are used as evaluation indexes. Experimental results show that the proposed method can obtain high-dimensional physical field distribution based on limited measurement data.