An Internet of Things-based Predictive Maintenance Architecture for Intensive Care Unit Ventilators

Intensive care units commonly utilize mechanical ventilators to treat patients with different medical conditions, which are crucial for patient care and survival. ICU ventilators have evolved through four distinct generations, each displaying unique features. Despite progress made since the 1940s, contemporary designs are insufficient to meet the increasing needs of patients and hospitals. Malfunctions in mechanical ventilators pose significant dangers to patients, highlighting the importance of focusing on their safety, security, precision, and dependability. Our study aims to address the significant issue at hand. Furthermore, the IoT industry has garnered significant attention because of rapid progress in smart devices, sensors, and actuators. The healthcare industry has seen a notable increase in health data as a result of the growing utilization of IoT and cloud computing technologies. To enhance growth, new models and distributed data analytics strategies must be developed to fully utilize the value of the vast datasets generated, including the incorporation of embedded machine learning. The study focuses on conducting Pareto and Failure Modes and Effects Analysis (FMECA) on ventilators in a specific hospital’s ICU, specifically those manufactured by the same company and unit. The analysis aims to identify the most critical and failure-prone component. Subsequently, we propose an IoT-focused framework for a predictive maintenance system implemented at the component level. The architecture comprises a monitoring framework and a data analytics module to predict potential system failures in advance, enhancing overall reliability.

@article{Manchadi2024,
  title     = {An Internet of Things-based Predictive Maintenance Architecture for Intensive Care Unit Ventilators},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {2},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Oumaima Manchadi and Fatima-Ezzahraa BEN-BOUAZZA and Zineb El Otmani Dehbi and Aymane Edder and Idriss Tafala and Mehdi Et-Taoussi and Bassma Jioudi},
  doi       = {10.14569/IJACSA.2024.0150294},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150294}
}