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.

中文翻译：

---

时间序列扩散法：用于振动信号生成的去噪扩散概率模型


扩散模型在图像生成等各个研究领域展示了强大的数据生成能力。然而，在振动信号生成领域，评价生成信号质量的标准与图像生成的标准不同，两者之间存在根本区别。目前还没有关于扩散模型产生振动信号能力的研究。在本文中，利用扩散模型的基本原理，提出了一种用于生成振动信号的时间序列扩散方法（TSDM）。 TSDM采用改进的U-net架构，具有注意力块、ResBlock和TimeEmbedding，可以有效地从一维时间序列数据中分割和提取特征。它基于前向扩散和反向降噪过程来生成时间序列。使用单频、多频数据集和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和多频特征，并保留轴承故障序列扩散生成结果的基本频率特征。研究还发现，原始的DDPM无法生成高质量的振动信号，而TSDM中改进的U-net，应用注意力块和ResBlock的组合，可以有效提高振动信号生成的质量。最后，将TSDM应用于三个公共轴承故障数据集的小样本故障诊断，结果表明三个数据集的小样本故障诊断准确率最多分别提高了32.380%、18.355%和9.298%。