Motor-bearing fault diagnosis is critical for industrial equipment reliability, yet traditional data-driven methods require extensive labeled data, which are often scarce in real-world applications. To address this challenge, we propose a Transformer transfer learning network (TTLN) for accurate fault diagnosis under cross-condition scenarios, particularly when target domain data are limited. First, we develop a Transformer-based fault diagnosis model that captures long-range dependencies in sequential data through self-attention, achieving high accuracy under single operating conditions. Second, we introduce the TTLN framework, which integrates domain adaptation to align marginal and conditional distributions, enabling robust cross-condition fault diagnosis with minimal target domain samples. Finally, we validated the proposed method on the CWRU and PU datasets, demonstrating the TTLN’s superior performance in data-scarce scenarios. For example, the TTLN achieved over 95% accuracy with only 100 target samples, outperforming traditional methods and fine-tuning-based approaches. The results underscore the TTLN’s effectiveness in cross-condition fault diagnosis, offering a practical solution for industrial applications with limited labeled data.

For real-time simulation of wave and its three-dimensional characteristics, an effective method is presented. Firstly, the LOD quadtree method is taken for high-precision grid drawing and improved Gerstner model is used for water height field modeling. Secondly, use P-M spectral theory to determine the upper and lower waves frequency limits. Then solve amplitude with integration method and extend it to arbitrary spectral sampling. Finally, after determine waveform parameter based on wave spectrum, we realize simulation of high-precision and large scale dynamic of real-time ocean waves.