DIGITAL TWIN MODELS FOR PREDICTIVE MAINTENANCE OF MEDICAL EQUIPMENT
Sunil Thakur, Kapil Mundada, Sachin Anap, Ganesh Thorat, Prashant B. Patel, Yogesh Nagergoje
Abstract: Digital twin models have become an important enabler in predictive maintenance of medical equipment to meet the increased need of reliability, safety, and cost efficiency in the contemporary healthcare setting. The traditional maintenance approaches are still more reactive or time based that results in unplanned downtime, delayed clinical process, and higher operational cost. The proposed research intends to design and test a data driven digital twin-based framework that can keep a constant check, predict, and optimize the health of critical medical equipment, including MRI scanners, ventilators, infusion pumps, and the system of patient monitoring. The proposed methodology will combine multi sensor telemetry, equipment use history with fault history records to create a high fidelity virtual replica of physical assets. The degradation modeling, fault prediction and remaining useful life estimation utilizes advanced algorithms such as physics informed models, long short term memory networks, auto encoder based anomaly detection and gradient boosting classifiers. The proposed digital twin model achieved a fault-prediction accuracy of 96.4% and a false alarm rate of 3.2%. The LSTM-based model achieved a remaining useful life prediction accuracy of 96.3% at 150 training epochs. Moreover, the framework allows reducing the unplanned equipment downtimes by 31 percent and maintenance expenses by 27 percent on the whole. The paper identifies scalability, interoperability, and regulatory preparedness in the hospital deployment situation in other clinical settings.
Keywords: Digital twin, Predictive maintenance, Medical equipment, Fault prediction, Remaining useful life, Healthcare IoT
DOI: 10.24874/PES08.03.001
Recieved: 06.05.2026 Revised: 25.06.2026 Accepted: 30.07.2026
UDC:
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