# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 1 Introduction  
Critical components of large and complex equipment endure prolonged exposure to alternating loads, high pressure, high temperature, corrosion, and other complex conditions. The diverse and multi-stage nature in the damage evolution of key components presents significant challenges for the fault diagnosis of critical components [1]. The fault diagnosis needs to focus on extracting local fault feature information within a fixed receptive field while also considering the global key fault features along the temporal dimension. This is crucial for enhancing the extraction of subtle and latent failure features during the damage evolution process [2]. An excellent fault diagnosis model should be capable of effectively extracting both global and local feature information. However, the majority of current research focuses on single aspects, with fewer models addressing the comprehensive consideration of both global and local fault information [3]. In recent years, many researchers have individually investigated the application of dilated convolutional networks [4] and the deep learning model Transformer [5] in the diagnosis of faults in key components, achieving certain results. For example, [PERSON] et al. [6] (2021) introduced the attention module Squeeze-and-Excitation (SE) and proposed a one-dimensional dilated convolutional network with residual connections for diagnosingfaults in rolling bearings. [PERSON] et al. [7] (2022) proposed a one-dimensional dilated convolutional neural network (1-DDCNN) for system fault diagnosis to address the limitations in feature information extraction and the issue of inaccurate diagnoses associated with traditional 1D-CNN. [PERSON] et al. [8] (2023) utilized dilated convolutions to expand the network's receptive field, coupled with Efficient Channel Attention (ECA), to extract essential fault feature information for improving fault diagnosis performance. However, since fault data often contain temporally dependent information, dilated convolutions are unable to capture global key fault features along the temporal dimension, focusing solely on local fault information within a fixed receptive field [9]. [PERSON] et al. [5] (2017) introduced the renowned deep learning model Transformer for handling sequential data. It incorporated self-attention mechanisms that allow the model to assign different attention weights at various positions in the sequence, enhancing the extraction of global information [10]. Subsequently, [PERSON] et al. [11] (2022) proposed a fault diagnosis method based on the Vision Transformer model. [PERSON] et al. [12] (2023) studied a fault diagnosis method for rolling bearings based on the one-dimensional Vision Transformer, achieving direct end-to-end diagnostic output. [PERSON] et al. [13] (2023) combined the explicit feature extractor FFT with an improved Transformer, proposing a more efficient bearing fault diagnosis model called Diagnisformer. However, transformer models exhibit relatively weaker processing capabilities for local information [14]. They may not effectively extract fault features with localized abrupt changes, potentially overlooking crucial local fault information. To better extract both global and local fault feature information, studies in the literature [15; 16; 17] have integrated both convolutional networks, which excel in local feature extraction, and Transformer models in their research. [PERSON] et al. [15] (2023) proposed combining the strengths of CNN and Transformer models to capture both local and long-range temporal dependencies in vibration signals. [PERSON] et al. [16] (2023) initially designed an efficient convolutional module to extract low-level local features and then used a transformer to extract global fault information. [PERSON] et al. (2023) [17] proposed the CNN-Transformer model (C-Trans) by integrating the advantages of multiscale CNN and transformer structures. The structures proposed in the above-mentioned literature currently only combine convolutional networks and Transformers in a simplistic manner.