###### Abstract
To address the issue of traditional feature pattern decomposition parameters being easily affected, they introduced the Scorpion Optimization Algorithm [20] to adaptively optimize key parameters. [PERSON] et al. [21] proposed a transformer framework based on the Fast Fourier Transform (FFT) [22] for mechanical fault diagnosis. The FFT-Trans innovatively extended the transformer global information exchange mechanism from the time domain to the frequency domain, employing a Global Frequency Encoding Layer [23] to mine potential fault features. [PERSON] et al. [24] proposed a deep learning method guided by sound-vibration physical-information fusion constraints, designing a lightweight transformer model. By constructing a bearing fault dynamics model and particle filter calibration parameters, they achieved the weighted fusion of multi-physical information from sound and vibration. However, traditional models still face numerous challenges when handling time-series data, particularly issues such as limited feature extraction ability and training instability, which lead to suboptimal performance when dealing with complex and diverse industrial data. This is especially true for deep networks, where gradient issues can cause training failure or performance degradation [25,26]. Although transformers show certain advantages in fault diagnosis tasks, they have limitations in modeling local dependencies and struggle to efficiently learn local features [27,28]. Therefore, to better handle time-series data and improve model performance, converting time-series data into 2D images has become particularly necessary. This transformation process effectively improves the feature extraction ability of time-series data and increases adaptability across different scenarios, thus solving the stability issues of traditional models in industrial applications. Research has shown that converting time-series signals into 2D images is an effective strategy that significantly improves the generalization performance of deep learning methods [29, 30, 31, 32, 33]. Image-based fault diagnosis methods typically involve converting time-series signals into image forms and utilizing deep learning models to extract relevant fault features from them. Common signal transformation methods include the symmetric dot pattern [34], Gramian Angular Field (GAF) [35], and Markov Transition Field [36], which employed various mathematical transformations to convert time-series data into 2D images, enabling effective feature extraction and fault diagnosis. [PERSON] [37] proposed a bearing fault diagnosis method based on the minimal unscented Kalman filter-assisted [38] deep belief network, converting multi-sensor signals into 2D feature maps using Gramian angles and summation fields and combining adaptive noise scaling and minimal unscented transformation for dynamic parameter adjustment. [PERSON] [39] and others proposed an integrated deep learning network. The 1D channel processed raw one-dimensional data using Long Short-Term Memory (LSTM) [40] and multi-head self-attention mechanisms, while the 2D channel obtained two-dimensional images through the recurrence attention map method and extracted features. The feature information from both channels was combined through feature fusion methods. To improve the limitations of traditional methods in bearing fault classification and better extract features, this paper proposed a method based on the ViT [41] model, which converts time-series data into 2D images for deep learning, aiming to enhance classification accuracy and model robustness. This paper innovatively proposes a method combining polar coordinate transformation with the Gram matrix to convert time-series data into two-dimensional images, thereby fully extracting data features and enhancing the stability of information representation, which significantly improves the reliability and robustness of fault classification.