Fault diagnosis of rotating machinery is an important engineering problem. In recent years, fault diagnosis methods based on the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been mature, but Transformer has not been widely used in the field of fault diagnosis. To address these deficiencies, a new method based on the Time Series Transformer (TST) is proposed to recognize the fault mode of bearings. In this paper, our contributions include: Firstly, we designed a tokens sequences generation method which can handle data in 1D format, namely time series tokenizer. Then, the TST combining time series tokenizer and Transformer was introduced. Furthermore, the test results on the given dataset show that the proposed method has better fault identification capability than the traditional CNN and RNN models. Secondly, through the experiments, the effect of structural hyperparameters such as subsequence length and embedding dimension on fault diagnosis performance, computational complexity and parameters number of the TST is analyzed in detail. The influence laws of some hyperparameters are obtained. Finally, via t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction method, the feature vectors in the embedding space are visualized. On this basis, the working pattern of TST has been explained to a certain extent. Moreover, by analyzing the distribution form of the feature vectors, we find that compared with the traditional CNN and RNN models, the feature vectors extracted by the method in this paper show the best intra-class compactness and inter-class separability. These results further demonstrate the effectiveness of the proposed method.

中文翻译：

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一种基于时序变压器的旋转机械故障诊断新方法

旋转机械故障诊断是一个重要的工程问题。近年来，基于卷积神经网络（CNN）和循环神经网络（RNN）的故障诊断方法已经成熟，但Transformer在故障诊断领域并未得到广泛应用。针对这些不足，提出了一种基于时间序列变换器（TST）的轴承故障模式识别新方法。在本文中，我们的贡献包括：首先，我们设计了一种可以处理一维格式数据的标记序列生成方法，即时间序列标记器。然后，介绍了结合时间序列标记器和 Transformer 的 TST。此外，在给定数据集上的测试结果表明，所提出的方法比传统的 CNN 和 RNN 模型具有更好的故障识别能力。其次，通过实验，详细分析了子序列长度、嵌入维数等结构超参数对TST故障诊断性能、计算复杂度和参数个数的影响。得到了一些超参数的影响规律。最后，通过 t-Distributed Stochastic Neighbor Embedding (t-SNE) 降维方法，将嵌入空间中的特征向量可视化。在此基础上，对TST的工作模式进行了一定的解释。此外，通过分析特征向量的分布形式，我们发现与传统的CNN和RNN模型相比，本文方法提取的特征向量表现出最好的类内紧凑性和类间可分离性。这些结果进一步证明了所提出方法的有效性。