# TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
## 5 Conclusions and Future Work  
This study proposed an end-to-end fault diagnosis model with high accuracy for rotating machinery based on a Vision Transformer, TSViT, and designed a time series patch embedding method which enables the TSViT to process raw vibration signals without any preprocessing. The model seamlessly integrates the transformer and CNN, addressing the limited receptive field issue of convolution kernels while effectively capturing long-term dependencies in vibration signals. After multiple experiments, the TSViT achieved an unprecedented 100% average accuracy on the PBR and XJTU test sets and 99.99% on the CWRU test set, demonstrating its excellent capability of fault diagnosis for rotating machinery. The experimental results validate its effectiveness under various working conditions also. This model can not only improve the accuracy of fault diagnosis for rotating machinery but also is more efficient than other fault diagnosis models based on Vision Transformers. The implementation of this model can detect potential problems with equipment in a timely manner. This paper also analyzes the influence of the model's hyperparameters through experiments, which provides valuable insights for researchers, facilitating the adoption of Vision Transformers in their work, such as in speech recognition, genomic analyses, drug development, and so on. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**CWRU Dataset**} \\ \hline Methods & Preprocessing & Accuracy \\ \hline WDCNN & No & 90.11\% \\ LSTM & No & 99.43\% \\ CNN-LSTM & No & 98.58\% \\ TST & No & 99.91\% \\ MCF-1 DViT & No & 99.83\% \\ Integrated ViT & DWT + CWT & 99.87\% \\ ECTN & STFT & 99.62\% \\ TSViT & No & 99.99\% \\ \hline \multicolumn{3}{c}{**PBR Dataset**} \\ \hline Methods & Preprocessing & Accuracy \\ \hline WDCNN & No & 73.50\% \\ LSTM & No & 98.30\% \\ CNN-LSTM & No & 93.68\% \\ TST & No & 99.98\% \\ TSViT & No & 100\% \\ \hline \multicolumn{3}{c}{**XJTU Dataset**} \\ \hline Methods & Preprocessing & Accuracy \\ \hline WDCNN & No & 99.68\% \\ LSTM & No & 100\% \\ CNN-LSTM & No & 99.98\% \\ TST & No & 100\% \\ TSViT & No & 100\% \\ \hline \hline \end{tabular}
\end{table} 
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