This paper presents the Real-time Adaptive and Interpretable Detection (RAID) algorithm. The novel approach addresses the limitations of state-of-the-art anomaly detection methods for multivariate dynamic processes, which are restricted to detecting anomalies within the scope of the model training conditions. The RAID algorithm adapts to non-stationary effects such as data drift and change points that may not be accounted for during model development, resulting in prolonged service life. A dynamic model based on joint probability distribution handles anomalous behavior detection in a system and the root cause isolation based on adaptive process limits. RAID algorithm does not require changes to existing process automation infrastructures, making it highly deployable across different domains. Two case studies involving real dynamic system data demonstrate the benefits of the RAID algorithm, including change point adaptation, root cause isolation, and improved detection accuracy.

翻译：本文提出了实时自适应可解释检测（RAID）算法。该创新方法解决了面向多变量动态过程的先进异常检测方法的局限性，即这些方法仅能检测模型训练条件范围内的异常。RAID算法能够适应非平稳效应（如数据漂移和突变点），而这些效应可能未在模型开发过程中被考虑，从而延长了系统的服务寿命。基于联合概率分布的动态模型用于处理系统中的异常行为检测，并基于自适应过程限值进行根因隔离。RAID算法无需更改现有的过程自动化基础设施，使其能够高度适用于不同领域。基于真实动态系统数据的两项案例研究展示了RAID算法的优势，包括突变点适应、根因隔离以及检测精度的提升。