# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 2 Materials and Methods  
Dilated convolution was first proposed by [PERSON] et al. [4]; it expands the receptive field without increasing the computational load by inserting holes between the convolution kernels. This approach enables the model to capture contextual information over a larger range while extracting local detail features. Its advantage in fault diagnosis lies in its ability to effectively extract small or local fault features. Especially when the signal is non-stationary or noisy, dilated convolution can preserve high-resolution detail information and help detect early or subtle failure modes. On the other hand, the multi-head attention mechanism in the Transformer processes multiple attention heads in parallel, with each head focusing on different parts of the data from various perspectives. Through weighted aggregation, it achieves comprehensive modeling of global information. The advantage of this mechanism in fault diagnosis is that it can capture global trends and patterns in equipment operation as a whole, making it particularly suitable for processing long-time series data [19] or multi-dimensional complex signals. In summary, the local information extraction of dilated convolution and the global information extraction of Transformer multi-head attention complement each other in fault diagnosis tasks. They can handle both minor faults and macro anomalies at the same time, which helps to improve the model's fault identification capabilities for complex equipment components.  
In this study, a multi-head, multi-scale dilated convolution module was designed to align with the structure of the multi-head attention mechanism, which was integrated into the Vision Transformer architecture. This established a fault diagnosis model for rotating components of large equipment, allowing for the simultaneous extraction of local fault features under fixed receptive fields and global key fault features across the temporal dimension. This approach enhances the model's accuracy in extracting subtle and latent failure characteristics associated with the damage evolution process of rotating components. The proposed model, as shown in Figure 1, consists of a data preprocessing module, the input embedding module, position encoding module, feature extraction module, sequence feature fusion module, and classifier module. The input data first proceed to the data preprocessing module, where they are standardized using the Z-score method. Subsequently, they are directed to the input embedding module, which expands and reconstructs the input data into a two-dimensional feature map. The resulting features then enter the positional encoding module, where trainable positional encodings are applied sequentially to the input sequence. Following this, the feature extraction module is employed to extract both local and global fault feature information. Finally, the output feature undergoes processing in the sequence feature fusion module and classifier module, resulting in the output of the classification matrix, thereby implementing fault diagnosis. The specific structures and implementation principles of each part are described below.