IoT-Powered Predictive Maintenance Framework for ICU Ventilators

Mechanical ventilators are essential in the intensive care unit (ICU) for treating and ensuring the survival of patients with multiple medical conditions. Despite the progress made by four generations of ICU ventilators, their breakthroughs have not been able to meet the evolving demands of patients and hospitals. The extent of the problem is that mechanical ventilator malfunctions present unacceptable dangers to patients, emphasizing the crucial need to guarantee their safety. Our study will address this concern through performing Pareto and Failure Modes and Effects Analysis (FMECA) on specific ventilators from different hospitals. The evaluations try to pinpoint the most crucial components susceptible to failure. We suggest an Internet of Things (IoT) architecture for a predictive maintenance system that operates at the component level. The suggested design includes a monitoring framework and a data analytics module that offer a comprehensive perspective on mechanical ventilators, enabling users to predict and deal with possible system malfunctions in advance. This work is motivated by the limitations of current ventilator designs and the growing interest in the Internet of Things (IoT) field, fueled by rapid technological progress in smart devices, sensors, and actuators.