  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.
