... remains extremely challenging due to the curse of dimensionality. To address this challenge, this paper introduces a deep learning-based multifidelity surrogate modeling approach that fuses multifidelity datasets for high-dimensional reliability analysis of complex structures. It first involves...

... benefits of the entire system. Against this backdrop, addressing the core challenges in the field of bearing fault diagnosis, an innovative fault diagnosis method has been proposed. For the first time, the Swin Transformer deep learning model is combined with acoustic emission (AE) technology, and through...

... and supervised machine learning and deep learning has profoundly accelerated the probability of failure (PoF) assessment and analysis. K-means clustering and Gaussian mixture models show direct relevance between the corrosion depth and corrosion rate, while the overlapping PoF value is scattered in three...