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<p>Rotating machinery is an important part of modern industry, and bearings are one of the most important things. However, bearing fault data are difficult to collect, and bearing fault diagnosis under small samples has significant research potential. In this paper, we proposed a fault diagnosis framework that combines diffusion modeling and improved Vision Transformer. First, the short-time Fourier transform is applied to the original one-dimensional vibration signals to convert the data into time-frequency maps. Second, the conditional diffusion model was applied to generate the required samples and expand the dataset. Finally, the Long-patch Vision Transformer (LVT) proposed in this paper is used to classify the mixed samples. LVT designs a long-patch division method for time-frequency maps with dense transverse features. The LVT contains denser features in each patch, and this method is more suitable for time-frequency maps. Validating the method proposed in this paper on two datasets and comparing it with other methods, our method achieved the highest accuracy among the compared methods.</p>

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Intelligent fault diagnosis method based on data generation and long-patch vision transformer under small samples

  • 期刊论文

  • 发表日期：

    2025年4月11日

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  • Jian Cen,
  • Weiwei Si,
  • Xi Liu,
  • Bichuang Zhao,
  • Hankun Huang,
  • Junfu Liu

摘要

Rotating machinery is an important part of modern industry, and bearings are one of the most important things. However, bearing fault data are difficult to collect, and bearing fault diagnosis under small samples has significant research potential. In this paper, we proposed a fault diagnosis framework that combines diffusion modeling and improved Vision Transformer. First, the short-time Fourier transform is applied to the original one-dimensional vibration signals to convert the data into time-frequency maps. Second, the conditional diffusion model was applied to generate the required samples and expand the dataset. Finally, the Long-patch Vision Transformer (LVT) proposed in this paper is used to classify the mixed samples. LVT designs a long-patch division method for time-frequency maps with dense transverse features. The LVT contains denser features in each patch, and this method is more suitable for time-frequency maps. Validating the method proposed in this paper on two datasets and comparing it with other methods, our method achieved the highest accuracy among the compared methods.