  The Industrial Internet of Things (IIoT) integrates interconnected sensors
and devices to support industrial applications, but its dynamic environments
pose challenges related to data drift. Considering the limited resources and
the need to effectively adapt models to new data distributions, this paper
introduces a Continual Learning (CL) approach, i.e., Distillation-based
Self-Guidance (DSG), to address challenges presented by industrial streaming
data via a novel generative replay mechanism. DSG utilizes knowledge
distillation to transfer knowledge from the previous diffusion-based generator
to the updated one, improving both the stability of the generator and the
quality of reproduced data, thereby enhancing the mitigation of catastrophic
forgetting. Experimental results on CWRU, DSA, and WISDM datasets demonstrate
the effectiveness of DSG. DSG outperforms the state-of-the-art baseline in
accuracy, demonstrating improvements ranging from 2.9% to 5.0% on key datasets,
showcasing its potential for practical industrial applications.
