A multi-model architecture based on deep learning for aircraft load prediction

Monitoring aircraft structural health with changing loads is critical in aviation and aerospace engineering. However, the load equation needs to be calibrated by ground testing which is costly, and inefficient. Here, we report a general deep learning-based aircraft load model for strain prediction and load model calibration through a two-phase process. First, we identified the causality between key flight parameters and strains. The prediction equation was then integrated into the monitoring process to build a more general load model for load coefficients calibration. This model achieves a 97.16% prediction accuracy and 99.49% goodness-of-fit for a prototype system with 2 million collected flight recording data. This model reduces the effort of ground tests and provides more accurate load prediction with adapted aircraft parameters. Analysis of the aircraft structural load needs costly and inefficient ground tests. Chenxi and coworkers report a deep learning based approach to predict aircraft strains and loads by identifying the key flight parameters in the load prediction, providing a more efficient and economical way for aircraft load monitoring.