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Technological advancements are the main drivers of the healthcare industry as it has a high impact on delivering the best patient care. Recent years witnessed unprecedented growth in the number of medical equipment manufactured to aid high-quality patient care at a fast pace. With this growth of medical equipment, hospitals need to adopt optimal maintenance strategies that enhance the performance of their equipment and attempt to reduce their maintenance cost and effort. In this work, we are proposing a Predictive Maintenance (PdM) strategy that relies on real-time data by using the Internet of Things (IoT) technology to help in the diagnosis of the failure. The proposed approach involves maintenance logs analysis, criticality assessment, failure modes analysis, feature selection, and machine learning implementation. The proposed approach has to be economically feasible and efficient in terms of selecting and monitoring the right parameters that reflect the health of the equipment. The approach was demonstrated using a case study from a local hospital-Sharjah, where critical equipment of Vitros immunoassay was analyzed. The maintenance strategy was changed from corrective to predictive using wireless sensors that monitors vibration signals. Features extracted and selected are analyzed using Support Vector Machine (SVM) to detect the faulty condition. In terms of economics, our approach proved to provide significant cost savings that can reach up to 25% which is worth investing in. The approach is scalable and can be used across medical equipment in large medical centers.

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