In this paper, some progress and achievements in aircraft integrity requirements, structural health monitoring, load spectrum measurement and life assessment research were presented. Several concepts of structural health monitoring were analyzed and compared, and the basic flow chart for health monitoring and life prediction of an aircraft structure was given. The selection of control points, construction of load/strain equations and stress calculation of control points were also described. Reliable IAT (Individual Aircraft Tracking) and life monitoring methods and software for IAT were developed for a certain type of aircraft, and fatigue life prediction of an aging aircraft was conducted based on actual measurement of load spectrum. The main features such as damage calculation, life evaluation and result output were discussed, and the future research focuses relating to intelligent structural health monitoring were finally explored.

High-precision operational flight loads are essential for monitoring fatigue of individual aircraft and are usually determined by flight parameters. To tackle the nonlinear relationship between flight loads and flight parameters for more accurate prediction of flight loads, artificial neural networks have been widely studied. However, there are still two major problems, namely the training strategy and sensitivity analysis of the flight parameters. For the first problem, the gradient descent method is usually used, which is time-consuming and can easily converge to a local solution. To solve this problem, an extreme learning machine is proposed to determine the weights based on a Moore–Penrose generalized inverse. Moreover, a genetic algorithm method is proposed to optimize the weights between the input and hidden layers. For the second problem, a mean impact value (MIV) method is proposed to measure the sensitivity of the flight parameters, and the neuron number in the hidden layer is also optimized. Finally, based on the measured dataset of an aircraft, the proposed flight load prediction method is verified to be effective and efficient. In addition, a comparison is made with some well-known neural networks to demonstrate the advantages of the proposed method.

Substitutional elements are introduced to face-centered cubic (FCC) multi-principal element alloys (MPEAs) to effectively enhance the mechanical performance by solid solution strengthening and second-phase strengthening. Commonly, relatively large atomic radius elements introduced into the alloy matrix result in lattice distortion and hinder grain boundary migration, thus achieving matrix strengthening. However, owing to the complex compositions of MPEAs, different substitutional elements introduced affect the microstructure evolution behavior and corresponding strengthening effects. In this work, an abnormal grain growth behavior of Ni-Co-Cr-based MPEAs based on Al alloying was observed. Systematic annealing experiments combined with quantitative grain growth analysis were conducted to clarify the effects of Al, W, and Mo on grain boundary migration. The results show that substitutional Al reduces the apparent activation energy for grain growth, resulting in both a lower grain growth component (n = 2) and a lower activation energy for grain growth of 219 kJ/mol, thereby enhancing grain boundary mobility. On the contrary, minor additions of high-melting-point W and Mo effectively inhibited the Al-induced rapid grain growth by increasing the activation energy and resulting in a higher grain growth component and a lower activation energy for grain growth of 251 kJ/mol. These findings provide new insights into the role of substitutional solutes in controlling grain growth kinetics in multi-principal element alloys.