An AIGC-driven Score-Based Diffusion Approach for Industrial Time Series

In the context of Industrial Internet of Things (IIoT), time series data is essential for maintenance and operational efficiency. However, challenges in IIoT data transmission, such as network instability, and in data annotation, like the high costs, lead to a shortage of high-quality labeled data, hindering system performance and industrial intelligence. Although traditional methods such as signal imputation and denoising have been employed, generative artificial intelligence (GAI) offers new possibilities for the generation of industrial time series data. To address these challenges, we propose a novel score-based diffusion architecture specifically designed for industrial data generation. The score-based approach effectively leverages the gradients of the data distribution, offering a more structured and stable generative process compared to GANs. Furthermore, our model incorporates Predictive-Corrective (PC) samplers with Langevin Dynamics Annealing to further optimize the generation process. Experimental results on turbofan engine datasets demonstrate that our model overcomes the inherent training instabilities of GANs, providing a more reliable and effective method for synthesizing high-fidelity industrial time series data.