Dynamic Model Interpretation-Guided Online Active Learning Scheme for Real-Time Safety Assessment
提出一种动态模型解释引导的在线主动学习方案（DMI-LS），利用可解释人工智能生成的解释来设计查询策略，以应对工业非平稳环境中的概念漂移，并在蛟龙号深潜器数据上验证了其优于现有方法。
Chunk-level real-time safety assessment of dynamic systems is a critical component of industrial processes, which is essential to prevent hazards and reduce the risk of injury or damage to equipment and facilities, especially in nonstationary environments. In this context, multiple real and complex concept drifts are inevitable in industrial settings, making it crucial to understand their detection and adaptation processes. The incremental learning scheme should also be well considered. However, existing methods have certain limitations in dealing with such issues. In this article, a dynamic model interpretation-guided online active learning scheme, termed a dynamic model interpretation-guided learning scheme (DMI-LS), is proposed. Specifically, the model update strategy with chunk data is designed based on the implementation of the broad learning system. A novel query strategy is then investigated to consider the ranking preference difference, which relies on the interpretation generated by the explainable artificial intelligence method. Several experiments based on the JiaoLong deep-sea manned submersible data are conducted to verify the effects of the proposed DMI-LS. The results show that it outperforms the other advanced existing approaches with different settings in most scenarios.