MACHINE LEARNING-BASED FAILURE PREDICTION MODELS FOR PREDICTIVE MAINTENANCE OF MEDICAL EQUIPMENT

Abstract

This study presents machine learning-based failure prediction models for predictive maintenance of medical equipment. The reliability, availability, and safety of medical devices are critical factors in healthcare service quality, particularly for life-supporting and diagnostic equipment such as ventilators, infusion pumps, defibrillators, ultrasound systems, X-ray devices, patient monitors, and laboratory analyzers. Traditional maintenance strategies, including reactive and scheduled preventive maintenance, are often insufficient because they do not fully consider real-time device condition, usage intensity, environmental stress, and historical failure patterns. To address these challenges, this study proposes a data-driven predictive maintenance framework based on machine learning. The proposed methodology includes medical equipment data acquisition, sensor-based condition monitoring, preprocessing of maintenance records, feature extraction, failure risk classification, remaining useful life estimation, anomaly detection, and maintenance decision support. Machine learning algorithms such as Random Forest, XGBoost, Support Vector Machine, Long Short-Term Memory networks, autoencoders, and hybrid ensemble models are considered for predicting potential failures before they occur. The results indicate that predictive maintenance can reduce unexpected downtime, improve equipment availability, optimize maintenance scheduling, and support clinical engineering decision-making. By integrating IoT-based monitoring, historical maintenance records, and machine learning models, healthcare institutions can move from reactive maintenance toward intelligent and condition-based asset management. The proposed framework is especially relevant for high-risk medical equipment where sudden failure may interrupt clinical workflow or threaten patient safety.