As aviation technology advances, numerous new aircraft enter the market. These not only offer airlines technological and fuel efficiency advantages but also present the challenge of how to conduct pilots’ aircraft-type transition training efficiently and economically. To address this issue, this study designed a methodology to quantitatively assess the similarity in panel display control design and standard operating procedures (SOPs) between aircraft types. Then, by combining the results of a questionnaire survey on A320, A330, B737, and B777 transition training and training cost data, it was verified quantitatively that inter-aircraft similarity has a positive impact on reducing the difficulty and cost of transition training. Taking the similarity in aircraft types as a feature, the KNN algorithm was used to successfully construct a difficulty prediction model for the training program of aircraft-type transition training. To overcome the limitation of insufficient training cost data volume, this study adopts the transfer learning method to construct a prediction model of the transition training cost, and the final significant prediction accuracy proves the effectiveness of the method. The research in this paper not only provides strong data support for the resource planning and cost management of airlines’ aircraft-type transition training but also provides new research perspectives and methodological guidance for the field of aviation training.

Structural Health Monitoring (SHM) is now essential for certifying many composite primary structures as it resolves strain redistribution at the moment of impact. Traditional detection methods, including resistive strain gauges, face challenges due to susceptibility to electromagnetic noise, as well as increased mass and wiring complexity proportional to the number of channels. This study proposes an impact strain monitoring and simulating method using optical fiber sensors for composite skin panels. Repeated low-velocity impact tests were conducted on large-size composite skin panels using various impact forces and locations. The 95% confidence interval for unit load strain in the simulation results differs from the experiment by 18%. This method effectively facilitates the monitoring of global impact strain on large-size composite skin panels.