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

通过改进 U-net 架构并结合扩散模型的基本原理，本研究提出了一种时间序列扩散方法 (TSDM)，通过前向扩散和逆向去噪过程对时间序列进行生成，并在振动信号生成、特征提取和小样本故障诊断等方面进行了实验证明。结果显示，TSDM 能够准确生成时序中的单频和多频特征，并能在扩散生成的轴承故障序列中保留基本频率特征，同时可提高三个公共轴承故障数据集的小样本故障诊断准确率。

时间序列扩散方法：用于振动信号生成的降噪扩散概率模型

Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model  for Vibration Signal Generation

In modern industries, fault diagnosis has been widely applied with the goal
of realizing predictive maintenance. The key issue for the fault diagnosis
system is to extract representative characteristics of the fault signal and
then accurately predict the fault type. In this paper, we propose a Causal
Disentanglement Hidden Markov model (CDHM) to learn the causality in the
bearing fault mechanism and thus, capture their characteristics to achieve a
more robust representation. Specifically, we make full use of the time-series
data and progressively disentangle the vibration signal into fault-relevant and
fault-irrelevant factors. The ELBO is reformulated to optimize the learning of
the causal disentanglement Markov model. Moreover, to expand the scope of the
application, we adopt unsupervised domain adaptation to transfer the learned
disentangled representations to other working environments. Experiments were
conducted on the CWRU dataset and IMS dataset. Relevant results validate the
superiority of the proposed method.

在现代工业中，故障诊断被广泛应用于实现预测性维护。本文提出了一种因果分离隐马尔可夫模型 (CDHM)，通过学习承载故障机制的因果关系，捕捉其特征以达到更强的表示能力。同时，我们利用时序数据逐步将振动信号分解为与故障相关和无关的因素，并通过重新优化 ELBO 来优化因果分离马尔可夫模型的学习。此外，为了扩大应用范围，我们采用无监督领域适应将学到的分离表示转移到其他工作环境。在 CWRU 数据集和 IMS 数据集上进行实验证实了该方法的优越性。