Diffusion models have demonstrated powerful data generation capabilities in various research fields such as image generation. However, in the field of vibration signal generation, the criteria for evaluating the quality of the generated signal are different from that of image generation and there is a fundamental difference between them. At present, there is no research on the ability of diffusion model to generate vibration signal. 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, ResBlock and TimeEmbedding 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. It is also found that the original DDPM could not generate high quality vibration signals, but the improved U-net in TSDM, which applied the combination of attention block and ResBlock, could effectively improve the quality of vibration signal generation. 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：扩散模型已在图像生成等多个研究领域展现出强大的数据生成能力。然而，在振动信号生成领域，生成信号质量的评估标准与图像生成不同，二者存在根本性差异。目前，尚无关于扩散模型生成振动信号能力的研究。本文基于扩散模型的基本原理，提出了一种用于振动信号生成的时间序列扩散方法（TSDM）。TSDM采用改进的U-net架构，结合注意力模块、残差块和时间嵌入技术，能有效分割和提取一维时间序列数据的特征。该方法基于前向扩散和反向去噪过程进行时间序列生成。实验使用单频、多频数据集和轴承故障数据集进行验证。结果表明，TSDM能够准确生成时间序列中的单频和多频特征，并在轴承故障序列的扩散生成结果中保留基本频率特征。研究还发现，原始DDPM无法生成高质量的振动信号，而TSDM中采用注意力模块与残差块组合的改进U-net能有效提升振动信号生成质量。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，结果显示三个数据集的小样本故障诊断准确率最高分别提升了32.380%、18.355%和9.298%。