This paper presents a study on the fatigue life prediction of notched fiber‐reinforced 2060 Al‐Li alloy laminates under spectrum loading by applying the constant life diagram. Firstly, a review on the state of the art of constant life diagram models for the life prediction of composite materials is given, which highlights the effect on the forecast accuracy. Then, the fatigue life of notched fiber‐reinforced Al‐Li alloy laminates (2/1 laminates and 3/2 laminates) is tested under cyclic stress, which has different stress cycle characteristics (constant amplitude loading and Mini‐Twist spectrum loading). The introduced models are successfully realized based on the available experimental data of examined laminates. In the case of Mini‐Twist spectrum loading, the effect of the constant life diagram on the life prediction accuracy of examined laminates is studied based on the rainflow‐counting method and Miner damage criteria. The results show that the simple Goodman model and piecewise linear model have certain advantages compared to other complex models for the life prediction of notched fiber metal laminates with different structures under Mini‐Twist loading. From the engineering perspective, the S‐N curve prediction based on the piecewise linear model is most applicable and accurate among all the models.

Rolling bearings, as essential parts of rotating machinery, face significant challenges in fault diagnosis due to limited fault samples and high noise interference, both of which reduce the effectiveness of traditional methods. To tackle this, this study proposes a fault diagnosis approach that combines Digital Twin (DT) and deep learning. First, actual bearing vibration data were collected using an experimental platform. After denoising the data, a high-fidelity digital twin system was built by integrating the bearing dynamics model with a Generative Adversarial Network (GAN), thereby effectively increasing the fault data. Next, the Wavelet Synchro-Extracting Transform (WSET) is used for high-resolution time-frequency analysis, and convolutional neural networks (CNNs) are employed to extract deep fault features adaptively. The fully connected layer of the CNN is then combined with a Least Squares Support Vector Machine (LSSVM), with key parameters optimized through an Improved Pelican Optimization Algorithm (IPOA) to improve classification accuracy significantly. Experimental results based on both simulated and publicly available datasets show that the proposed model has excellent generalizability and operational flexibility, surpassing existing deep learning-based diagnostic methods in complex industrial settings.

This article presents an economical and efficient way to assess the structural safety and fatigue reliability of the bolster, a key structure for the metro-train. First, its finite element model was validated based on the comparison between numerical results and experimental data of stresses and displacements recorded during a static test. Then, the fatigue life of the bolster under loading scheme was predicted using Gerber diagram, in which the validated finite element model was applied to determine the weak points, and its structural safety was also evaluated by a full-scale fatigue test and non-destructive test method. Finally, the metro-train bolster was modeled as a series system of weak points, and a new fatigue reliability model for mechanical component derived based on stress-life interference model was used to assess its fatigue reliability under the application of the design passenger number spectra. The results show that not only does this bolster satisfy the structural safety requirement during its service life, but also its fatigue reliability is more than 99.993% after 30-year service under normal atmospheric conditions.