A Data-Driven Framework for Anomaly Detection in Industrial Systems Using Log Data
Merle Hewing, Yuanchen Zhao, Manuel Vossel, Paul Nieschler, Tobias Kleinert · 2025
Reliability engineering plays a crucial role in modern industrial systems, aiming to minimize costly downtime and prevent safety hazards. The feasibility of automating this process largely depends on the available data. While sensor-level data analysis can reveal crucial insights into system health and operational states, often only event-driven log data is available due to technical or cost constraints. Although extensive research on log-based failure diagnosis has been conducted, particularly in the information technology (IT) sector, the application of these methods remains challenging in real-world industrial systems. Hence, we propose a modular and interpretable framework for log-based anomaly detection in industrial systems to address the interpretability and reliability shortcomings of previous approaches. The results obtained from a real-world production system validate the framework’s ability to support timely root cause analysis and facilitate predictive maintenance.