A Hybrid Data-Driven and Physics-Based Predictive Maintenance Framework for RF Subsystem Monitoring in Medical Cyclotrons

In an era where reliability, efficiency, and sustainability have become essential objectives across all sectors, the healthcare domain faces even greater challenges due to its direct impact on human life. Medical devices represent the technological backbone of modern healthcare systems, supporting activities ranging from diagnosis and monitoring to treatment and therapeutic intervention. Ensuring their continuous availability, safety, and performance is therefore a critical priority. Traditional maintenance strategies, however, often rely on reactive actions that may lead to unexpected downtime, increased operational costs, and potential risks to patient safety. To address these limitations, predictive maintenance has emerged as a promising and innovative solution. This study focuses on the development of a predictive maintenance framework for the RF subsystem of a medical cyclotron, identified as the most critical subsystem through a Failure Modes, Effects, and Criticality Analysis (FMECA). The proposed framework combines real operational log data with synthetic signals generated from a MATLAB Simulink (version 2023) physics-based model of the RF subsystem, and applies a Random Forest classifier trained on a degree-3 polynomial feature expansion of the three raw RF signals. Using a group-separated evaluation protocol that partitions contiguous acquisition sessions into independent fitting, calibration, validation, and test sets, the model achieves perfect fault-state classification (Recall = 100%, zero false alarms) on 58,291 held-out real operational samples containing 64 fault-state events, with the decision threshold selected exclusively on an independent validation partition. A complementary short-horizon anticipation task, using only causally available past information, evaluates the model’s ability to flag an impending threshold crossing up to 60 s in advance. The proposed approach provides a scalable and interpretable foundation for real-time predictive maintenance deployment in nuclear medicine facilities, while identifying the acquisition of independently verified maintenance records as a necessary next step toward validating true early failure prediction.