IoT Based Predictive Maintenance Management of Medical Equipment

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 costs and effort. In this work, a Predictive Maintenance (PdM) approach is presented to help in failure diagnosis for critical equipment with various and frequent failure mode(s). The proposed approach relies on the understanding of the physics of failure, real-time collection of the right parameters using the Internet of Things (IoT) technology, and utilization of machine learning tools to predict and classify healthy and faulty equipment status. Moreover, transforming traditional maintenance into PdM has to be supported by an economic analysis to prove the feasibility and efficiency of transformation. The applicability of the approach was demonstrated using a case study from a local hospital in the United Arab Emirates (UAE) where the Vitros-Immunoassay analyzer was selected based on maintenance events and criticality assessment as a good candidate for transforming maintenance from corrective to predictive. The dominant failure mode is metering arm belt slippage due to wear out of belt and movement of pulleys which can be predicted using vibration signals. Vibration real data is collected using wireless accelerometers and transferred to a signal analyzer located on a cloud or local computer. Features extracted and selected are analyzed using Support Vector Machine (SVM) to detect the faulty condition. In terms of economics, the proposed approach proved to provide significant diagnostic and repair cost savings that can reach up to 25% and an investment payback period of one year. The proposed approach is scalable and can be used across medical equipment in large medical centers.