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.

基于蒸馏的自主引导(DSG)模型利用生成式回放机制，通过知识蒸馏从先前基于扩散的生成器向更新的生成器传输知识，从而提高了生成器的稳定性和再现数据的质量，有效缓解了在工业流数据中出现的挑战，并在CWRU、DSA和WISDM数据集上取得了优于现有基准模型的实验结果，展示了在实践工业应用中的潜力。

基于扩散式生成回放的工业流数据连续学习