###### Abstract  
The stability of gearbox bearings is crucial to the operational efficiency and safety of industrial equipment, as their faults can lead to downtime, economic losses, and safety risks. Traditional models face difficulties in handling complex industrial time-series data due to insufficient feature extraction capabilities and poor training stability. Although transformers show advantages in fault diagnosis, their ability to model local dependencies is limited. To improve feature extraction from time-series data and enhance model robustness, this paper proposes an innovative method based on the ViT. Time-series data were converted into two-dimensional images using polar coordinate transformation and Gramian matrices to enhance classification stability. A lightweight front-end encoder and depthwise feature extractor, combined with multi-scale depthwise separable convolution modules, were designed to enhance fine-grained features, while two-dimensional rotary position encoding preserved temporal information and captured temporal dependencies. The constructed RoPE-DWTrans model implemented a unified feature extraction process, significantly improving cross-dataset adaptability and model performance. Experimental results demonstrated that the RoPE-DWTrans model achieved excellent classification performance on the combined MCC5 and HUST gearbox datasets. In the fault category diagnosis task, classification accuracy reached 0.953, with precision at 0.959, recall at 0.973, and an F1 score of 0.961; in the fault category and severity diagnosis task, classification accuracy reached 0.923, with precision at 0.932, recall at 0.928, and an F1 score of 0.928. Compared with existing methods, the proposed model showed significant advantages in robustness and generalization ability, validating its effectiveness and application potential in industrial fault diagnosis. industrial equipment; vit transformer; 2D-RoPE; fault diagnosis; feature extraction 2020201557260  
26 May 2025222 June 202525 June  
surface fatigue. Therefore, the proper selection of lubricant types, timely replacement, and maintenance of the lubrication system significantly reduces frictional wear and delayed fatigue crack propagation, thereby extending the service life of gearboxes and their bearings [1,2]. Based on this background, fault type prediction technology for gearbox bearings has become an important research direction in the field of industrial equipment health management. By deeply analyzing bearing operation data, potential fault types have been identified, improving the scientific basis and efficiency of equipment maintenance [4,5]. The application of fault prediction technology helps reduce unplanned downtime, lower maintenance costs, and improve production stability and economic benefits [6, 7, 8]. As industrial equipment operating conditions have become increasingly complex, deep learning-based fault prediction methods, known for their high accuracy and compatibility with diverse data types, have emerged as a core research direction, driving many researchers to adopt classic neural network models for fault diagnosis tasks [9,10]. [PERSON] et al. [11] employed a deep learning approach based on the InceptionV3 model [12], incorporating contrast-limited adaptive histogram equalization preprocessing and the Squeeze and Excitation (SE) channel attention mechanism [13], combined with support vector machine [14] classification, to achieve high-precision detection of motor faults. [PERSON] et al. [15] proposed a dual-pipeline deep learning model combining 1D convolutional neural networks (CNNs) [16] and recurrent neural networks (RNNs) [17], which extract spatial and temporal features of induction motor signals through a multi-head mechanism, effectively avoiding issues such as the CNN neglecting long-term dependencies. [PERSON] et al. [18] proposed a high-performance rolling bearing fault diagnosis method using adaptive feature pattern decomposition and a transformer [19].