In this paper, the damage monitoring investigation based on the remote bonding fiber Bragg grating sensing is performed on the aerospace aluminum alloy thin-walled structure with prefabricated damage. Firstly, an ultrasonic excitation-fiber Bragg gratings (UE-FBGs) sensing experimental platform is established for the simulation of defects monitoring, in which the sensors are placed at a certain distance from the bonding area. Secondly, different arrangements of exciters and receivers are utilized for the original signals and the damage signals. Subsequently, the raw signals are processed by filter and feature extraction in order to denoise the signals and acquire the parameters sensitive to the damage. Finally, an improved Reconstruction for Image Defects (RAPID) algorithm is used to locate and reconstruct the pre-existing damage. The results show that the proposed system improves the sensitivity of the FBG receiver signal and the accuracy of the damage imaging.

Load spectrum enhancement is a pivotal accelerated fatigue testing methodology employed to substantially reduce test duration and associated costs. This technique operates by strategically elevating load amplitudes while ensuring the preservation of the original failure mechanism. In this study, a novel fatigue life prediction model for variable amplitude loading is developed by integrating the theories of Equivalent Initial Flaw Size (EIFS) and the Equivalent Plastic Zone (EPZ). This integrated approach explicitly accounts for both the small crack effect and load interaction effects, which are critical yet often oversimplified aspects of fatigue damage accumulation. The model is subsequently applied to quantitatively establish the relationship between the Load Enhancement Factor (LEF) and the test time or compression ratio. Finally, fatigue tests on typical 2A14 aluminum alloy structures under variable amplitude loading are conducted to validate the proposed model. The results demonstrate a significant life reduction with increasing LEF, achieving a remarkable test time reduction of over 50% at an LEF of 1.2. All experimental data fall within a scatter band of three, relative to the model prediction. Additionally, the predicted mean compression ratio exhibits approximate agreement with the experimental data, with errors within an acceptable range. This work provides a physically grounded and practically validated framework for implementing efficient and reliable load spectrum enhancement.