  Rolling bearings are the most crucial components of rotating machinery.
Identifying defective bearings in a timely manner may prevent the malfunction
of an entire machinery system. The mechanical condition monitoring field has
entered the big data phase as a result of the fast advancement of machine
parts. When working with large amounts of data, the manual feature extraction
approach has the drawback of being inefficient and inaccurate. Data-driven
methods like the Deep Learning method have been successfully used in recent
years for mechanical intelligent fault detection. Convolutional neural networks
(CNNs) were mostly used in earlier research to detect and identify bearing
faults. The CNN model, however, suffers from the drawback of having trouble
managing fault-time information, which results in a lack of classification
results. In this study, bearing defects have been classified using a
state-of-the-art Vision Transformer (ViT). Bearing defects were classified
using Case Western Reserve University (CWRU) bearing failure laboratory
experimental data. The research took into account 13 distinct kinds of defects
under 0-load situations in addition to normal bearing conditions. Using the
short-time Fourier transform (STFT), the vibration signals were converted into
2D time-frequency images. The 2D time-frequency images are used as input
parameters for the ViT. The model achieved an overall accuracy of 98.8%.
