Diffusion models have demonstrated robust data generation capabilities in various research fields. In this paper, a Time Series Diffusion Method (TSDM) is proposed for vibration signal generation, leveraging the foundational principles of diffusion models. The TSDM uses an improved U-net architecture with attention block to effectively segment and extract features from one-dimensional time series data. It operates based on forward diffusion and reverse denoising processes for time-series generation. Experimental validation is conducted using single-frequency, multi-frequency datasets, and bearing fault datasets. The results show that TSDM can accurately generate the single-frequency and multi-frequency features in the time series and retain the basic frequency features for the diffusion generation results of the bearing fault series. Finally, TSDM is applied to the small sample fault diagnosis of three public bearing fault datasets, and the results show that the accuracy of small sample fault diagnosis of the three datasets is improved by 32.380%, 18.355% and 9.298% at most, respectively

翻译：扩散模型已在多个研究领域展现出强大的数据生成能力。本文提出了一种时间序列扩散方法（TSDM），用于振动信号生成，该方法基于扩散模型的基本原理。TSDM采用改进的包含注意力机制的U-net架构，有效分割并提取一维时间序列数据的特征。其运行基于前向扩散与反向去噪过程实现时间序列生成。利用单频、多频数据集以及轴承故障数据集进行实验验证。结果表明，TSDM能够准确生成时间序列中的单频与多频特征，并在轴承故障序列的扩散生成结果中保留基本频率特征。最后，将TSDM应用于三个公开轴承故障数据集的小样本故障诊断中，结果显示，三个数据集的小样本故障诊断准确率分别最高提升了32.380%、18.355%和9.298%。