Machine Learning-Based Anomaly Detection for ITER's Tokamak Systems Monitor: A Gyrotron Case Study

The Tokamak Systems Monitor (TSM) software will provide ITER operators with timely assessments of machine health, component lifetime, and early warnings of potential faults. Among its key functions, the TSM will employ data-driven anomaly detection methods to identify unexpected or abnormal behavior across a broad range of systems and diagnostics. This work presents an initial proof-of-concept for anomaly detection applied to gyrotron pulses, leveraging data from the European gyrotron prototype. The method combines dimensionality reduction and clustering techniques to identify deviations from expected operational patterns. This approach enables the detection of subtle anomalies that might otherwise go unnoticed. While this first demonstration focuses on intershot anomaly detection for gyrotrons, the methodology is designed to be adaptable to other systems and signals within the TSM, contributing to the improved maintenance strategies and reliability of the ITER tokamak.