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.

传统的卷积神经网络（CNN）在捕捉时间特征（即振动信号随时间的变化）方面存在局限性，为解决这个问题，本文引入了一种新颖的模型，即时间序列视觉变换器 (TSViT)，专门用于故障诊断。TSViT 模型一方面通过集成卷积层对振动信号进行分段并捕捉局部特征，另一方面采用变压器编码器来学习长时间序列的信息。在两个不同数据集上与其他方法进行的实验结果验证了 TSViT 的有效性和通用性，并对其超参数对模型性能、计算复杂度和整体参数数量的影响进行了比较分析。TSViT 在两个测试集上分别达到了 100% 和 99.99% 的平均准确率。