Enhancing Large Medical Equipment Health-Aware Control with Bayesian Graph Attention Transformer-Based Probabilistic...

Large medical equipment such as MRI, CT scanners, and linear accelerators are critical healthcare infrastructure. The health status of these systems directly impacts diagnHealth-Aware Control osis accuracy, treatment efficacy, and overall healthcare quality. However, current maintenance approaches often rely on fixed schedules or reactive strategies, leading to unnecessary downtime, increased costs, and potential patient care disruptions. This lack of uncertain information hinders robust decision-making in health-aware control (HAC) and maintenance scheduling. Therefore, this paper proposes a novel probabilistic remaining useful life (RUL) prediction method for large medical equipment based on a Bayesian graph attention transformer. First, we construct a graph attention network to extract complex non-Euclidean spatial relationships between multiple sensor signals from medical equipment. We then integrate this graph attention mechanism into the transformer's temporal multi-head attention module, combining graph attention networks' spatial feature extraction advantages with transformer models' temporal feature extraction capabilities. This integration enables joint extraction of spatiotemporal relationships in sensor data. Simultaneously, we employ an improved Bayesian network to quantify prediction uncertainty, providing point estimates of RUL and corresponding confidence intervals. The resulting framework supports HAC decisions that balance large medical equipment performance and longevity. Experiments conducted on two medical equipment operation datasets (CT and MRI) demonstrate the effectiveness and superiority of our approach compared to existing methods, achieving more accurate and reliable RUL predictions that enable optimized maintenance scheduling and enhanced HAC strategies.