A Structured Machine Learning Framework for Predictive Maintenance in Medical Imaging Systems

Medical imaging systems, such as computed tomography scanners, comprise highly integrated architectures of interdependent systems, subsystems, and components, where failures in any element can propagate across the system. Such disruptions lead to unplanned downtime, interruptions in clinical workflows, deferred patient examinations, and increased healthcare delivery costs. Traditional maintenance strategies are predominantly reactive, addressing failures only after they occur, which limits system reliability and compromises patient care. This reactive approach also results in inefficient resource utilization and potential unnecessary radiation exposure due to aborted scans.
We propose a structured machine learning framework for prognostics and health management, enabling predictive maintenance across imaging modalities. The approach integrates machine data (sensor data, system logs), operational data, and historical service records into a unified pipeline for Remaining Useful Life prediction. Advanced feature engineering techniques, including higher-order statistical transformations and drift detection metrics, capture latent degradation signatures within machine data. Degradation-specific feature selection, guided by hypothesis testing and domain expertise, isolates leading indicators from thousands of raw error codes, improving interpretability and robustness. The modeling strategy combines supervised learning with semi-supervised techniques, which address incomplete labeling in service ground truth data. Validation on historical and pilot deployments confirms scalability across product families and adaptability to software version variability.
This modular, scalable framework provides sufficient lead time for predictive service actions, reducing unplanned downtime and optimizing maintenance scheduling. By differentiating leading, lagging, and coincident log features, the approach enhances precision of predictions. Its adaptable design supports deployment across imaging modalities and extends to other industrial applications, offering a systematic and refined methodology for prognostics and health management.

Keywords: Predictive Maintenance, Machine Learning Framework, Remaining Useful Life Prediction, Prognostics and Health Management.