A Generic Framework for Multivariate Anomaly Detection and Root-Cause Analysis Using Slow and Fast Detection in Process Industries

Abstract

The digitalization of process industries via the Industrial Internet of Things (IIoT) has introduced challenges like poor generalization, lack of interpretability, and complex multi-frequency sensor data. Most existing systems struggle to simultaneously detect anomalies and trace their root causes, resulting in over-specialized, inflexible solutions. To address this, we present a generic framework for multivariate anomaly detection and root-cause analysis (RCA) featuring dual detection pathways. The Slow Anomaly Detection System (SADS) blends physics-based models with a data-driven LSTM to detect gradual anomalies in the absolute domain. Meanwhile, the Fast Anomaly Detection System (FADS) identifies abrupt deviations in the derivative domain. When anomalies occur, an RCA module identifies the probable input cause using model-weighted deviation scoring, which amplifies baseline deviations by their target sensitivity. Validated on an operational Reverse Osmosis (RO) plant dataset using synthetic anomaly injections, SADS showed high precision for severe anomalies, and both systems achieved strong AUC scores of 0.85–1.00. The framework is designed for transferability across continuous process industries through its modular architecture, with the physics-based component requiring the most substantial process-specific redevelopment; validation beyond the RO case study presented here is identified as a priority direction for establishing this transferability empirically.