MAGE: Multiperiodic Adaptive Graph Evolution Guided Anomaly Detection in Industrial IoT
Weixu Wang, Xiaobo Zhou, Tie Qiu, Lei Wang · IEEE Transactions on Industrial Informatics · 2025
Identifying and detecting anomalies in industrial Internet of Things (IIoT) systems is vital for maintaining industrial safety. In IIoT scenarios, various industrial machines operate with differing periods that overlap temporally, resulting in complex multiperiodic temporal patterns. In addition, varying production tasks and environmental conditions alter sensor dependencies, complicating the modeling of intersensor dependency topologies. Existing methods, which rely on a fixed global topologies, struggle to adapt to these complex multiperiodic temporal patterns and evolving dependency topologies, leading to low anomaly detection accuracy. To tackle these problems, we propose MAGE, a multiperiodic adaptive graph evolution guided anomaly detection framework. MAGE first segments sensor data into distinct temporal periods, then employs a dynamic graph structure learning module to model evolving dependencies. Finally, a global-local association discrepancy module is employed to enhance the anomaly detection capability. Comprehensive experiments on five real-world datasets demonstrate MAGE's superior performance compared to state-of-the-art approaches.