## 1 Introduction
Considering the issue of imbalanced data and time-varying features caused by variable working conditions, some of the latest methods improve CNN from the adaptive level [36; 37], some focus on data balance processing [38]. This is not ideal for fault diagnosis based on the current signal since its fault features are too weak. Moreover, when training on large-scale datasets, its calculation costs increase significantly. Recently, a new architecture that takes full advantage of an attention mechanism that can obtain global features containing different positions of the entire sequence to focus on sensitive features--Transformer--was proposed by Google's research team [39]. Due to the fact that the attention mechanism operation mode is almost completely different from convolution operation, this is a significant breakthrough in the field of NLP. Soon afterwards, Vision Transformer (ViT) [40] was proposed by the Google Research team to apply Transformer to image classification, which had received widespread attention from the academic community. Although it is not widely used in fault diagnosis at present, considering the special requirements of extracting sequence features and internal correlations during signal processing, the varieties of Transformer show extraordinary potential. For example, [PERSON] et al. of Southeast University [41] relied on their bearing experimental dataset to extract fault features through time-frequency analysis of vibration signals and input them into Transformer for fault diagnosis to verify the performance. [PERSON] et al. [42] proposed an integrated Vision Transformer model that incorporated the soft voting method for classification decisions, which can enable the model to perform bearing fault diagnosis more accurately. [PERSON] et al. [43] utilized the Siamese network to map feature vectors into a new space for faster and more effective fault feature extraction and finally combined it with Vision Transformer to achieve fault diagnosis. These studies have verified the effectiveness and superiority of the Transformer model. [PERSON] et al. [44] made a detailed comparative study on the fault diagnosis methods of rolling bearings by using different sensors and pointed out that the technology using artificial intelligence is still developing rapidly. The combination of time-domain statistics, frequency spectra, time-frequency spectra, and other image analyses with deep learning methods provides a new technique for bearing fault diagnosis. Through a comparative analysis of relevant research, combining the limitations of the above methods with the advantages of ViT [40], a bearing fault diagnosis method is proposed based on the instantaneous square current value and the Vision Transformer model (ISCV-ViT). And verify the competitiveness of the proposed method in solving bearing mechanical fault diagnosis problems based on MCS through case studies. At the signal processing level, the instantaneous square current value of the two-phase current is calculated to plot the instantaneous square current value-time curve as a feature extracted from the time domain. Subsequently, the time-domain diagram of the instantaneous square current value is input to the ViT model to perform fault classification. The main contributions of this study are summarized as follows:  
1. For the reason that it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors, the MCS is selected as the analysis subject. And proposed a processing method, ISCV, for fault feature extraction in MCS;
2. To solve the problem that traditional fault diagnosis methods cannot meet the requirements of diagnostic accuracy due to the low SNR of MCS, a diagnostic model based on ISCV-ViT is proposed for bearing diagnosis, which is evaluated on two datasets of rolling bearings to prove the model's accuracy and practicality, namely, the public bearing dataset from Paderborn University (PU) and the laboratory bearing dataset of Shenzhen Technology University (SZTU).