Anomaly Detection in Lithium-Ion Batteries via Stream Reasoning on Structured Knowledge and Time-Series Data

Monitoring complex systems is essential for preventing failures and ensuring operational safety. This paper proposes a method that combines ontology which provide structured knowledge representation with stream reasoning to enhance anomaly detection in dynamic environments. Unlike monitoring systems, our approach focuses on interpretable and scalable analysis of continuous data streams, enabling systematic identification of deviations from expected behavior. We demonstrate the applicability of this framework in monitoring lithium-ion batteries, where early detection of thermal anomalies is critical. By integrating a knowledge-driven model with data stream analysis, our method improves the reliability and safety of complex systems while offering explainable insights into detected anomalies.