Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingFault DiagnosisImage GenerationTime SeriesTime Series GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models
Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computationally expensive. We address this by combini…
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient. To this en…
Multivariate Time Series ForecastingProbabilistic Time Series ForecastingTime SeriesTime Series Analysis+1DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone
Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability to estimate uncertainty of imputation results. Meanwhile, denoising diffusion probabilistic models (DDPMs) have achi…
DenoisingImputationMambaTime SeriesNon-stationary Diffusion For Probabilistic Time Series Forecasting
Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing Denoising Diffusion Probabilistic Models (DDPMs) often fail to capture this non-…
DenoisingProbabilistic Time Series ForecastingTime SeriesTime Series ForecastingEffective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion
We propose the Fourier Adaptive Lite Diffusion Architecture (FALDA), a novel probabilistic framework for time series forecasting. First, we introduce the Diffusion Model for Residual Regression (DMRR) framework, which un…
Computational EfficiencyDenoisingProbabilistic Time Series Forecastingregression+2