Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model
  for Vibration Signal Generation
Haiming Yi Lei Hou Yuhong Jin Nasser A. Saeed
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