  Traditional fault diagnosis methods using Convolutional Neural Networks
(CNNs) face limitations in capturing temporal features (i.e., the variation of
vibration signals over time). To address this issue, this paper introduces a
novel model, the Time Series Vision Transformer (TSViT), specifically designed
for fault diagnosis. On one hand, TSViT model integrates a convolutional layer
to segment vibration signals and capture local features. On the other hand, it
employs a transformer encoder to learn long-term temporal information. The
experimental results with other methods on two distinct datasets validate the
effectiveness and generalizability of TSViT with a comparative analysis of its
hyperparameters' impact on model performance, computational complexity, and
overall parameter quantity. TSViT reaches average accuracies of 100% and 99.99%
on two test sets, correspondingly.
