Industrial Device Time Series Generation Using Conditional Diffusion
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Solution Overview
Problem
The generation of time series for industrial devices using existing GAN models is challenging due to convergence issues during training, leading to inefficiencies in generating accurate time series.
Innovation Solution
A conditional temporal diffusion model-based method and apparatus are employed to generate time series for industrial devices. This involves acquiring parameter indicator data, using a noise from a target Gaussian distribution as an initial variable, and iteratively inputting this data into a noise prediction model constructed based on a conditional temporal diffusion model to generate the time series.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a GAN model is used to generate time series of industrial devices, then the generation capability is provided, but the training process fails to converge and generation efficiency is low
Solution Approach 1:
The patent changes the fundamental parameters of the generative model from GAN to diffusion model, which operates on different mathematical principles. The diffusion model uses a forward diffusion process that gradually adds noise to data and a reverse denoising process that reconstructs data, fundamentally changing how generation works compared to GAN's adversarial approach. This parameter change resolves the convergence issue while maintaining generation capability
Solution Approach 2:
The patent replaces the adversarial mechanical system of GAN (generator vs. discriminator confrontation) with a probabilistic diffusion system. Instead of two networks competing, the diffusion model uses a unified framework with forward noise addition and reverse denoising processes, eliminating the inherent instability and convergence problems of the adversarial approach
2Manufacturing precision
If industrial device time series data is processed, then accurate generation is required, but the data has poor quality, high noise, and complex time dependencies making generation difficult
Solution Approach 1:
The patent segments the complex time series generation task into distinct temporal components using temporal decomposition. The model separates the time series into trend components (long-term patterns) and seasonal/components (short-term fluctuations), allowing each to be modeled independently with appropriate techniques, thereby handling complex time dependencies more effectively
Solution Approach 2:
The patent introduces conditional variables as intermediaries that capture domain knowledge and constraints about industrial device behavior. These conditional inputs guide the diffusion process to generate realistic time series that respect physical and operational constraints, improving accuracy without requiring the model to learn all complexities from scratch
Data Source
AI summary
A conditional temporal diffusion model-based method and apparatus for generating a time series of an industrial device, including: acquiring parameter indicator data for the time series of the industrial device; using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series; inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model; denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant; and inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device.