Optimizing Energy-Centered Maintenance for Medical Devices in Hospital Using K-NN Classification from Its Residual Current

Regular time-based preventive maintenance for medical devices often fails to detect actual component degradation. This study proposes a K-NN predictive framework that analyzes the residual current signal (IΔ) to categorize the operational conditions of medical devices across two representative device types: Syringe Pump and Patient Monitor. The raw signal was transformed into a higher-dimensional feature space, consisting of mean, standard deviation, gap, and RMS, to handle its characteristics. By evaluating various distance metrics, the results show that Cosine provides the most efficient diagnostic path, achieving optimal factor (fopt) at a lower number of neighbor parameters, at K = 4 for Syringe Pump and K = 8 for Patient Monitor with an accuracy of 94.21% and 94.41%, respectively. The disparity in K-values reflects the inherent model complexity resulting from distinct power supply architectures, a characteristic also manifested in reactive power (Q). By mapping this statistical transformation, the model overcomes the limitations of static threshold-based leakage current monitoring. This research marks a paradigm shift towards a data-driven Energy-Centered Maintenance (ECM) strategy. By facilitating interventions triggered by empirically assessed signal signatures rather than predetermined time intervals, this framework optimizes maintenance activities and enhances the overall energy efficiency of hospital infrastructure.