Machine Learning for Predictive Maintenance: A Cloud Computing Architecture and Lessons for a Healthcare Context

Abstract

In recent years, hospitals have invested substantially in new medical technology to guarantee medical devices'
precision, reliability, and requisite performance. Despite technological developments revolutionizing medical devices,
antiquated maintenance practices persist in healthcare services and infrastructure. Maintenance plans frequently need
to be created for a combination of cutting-edge and outdated technology utilized in medical devices. Many hospitals
have encountered the issue of identifying equipment-related risks that have been mitigated by implementing
appropriate integrity monitoring techniques. The incessantly increasing volumes of extensive data streams gathered
from actuators and sensor assemblies integrated into network-capable detectors and chips of medical devices
necessitate a scalable platform construction to facilitate the requisite storage and immediate data processing for device
tracking and upkeep. This study examines the maintenance of medical devices via an Internet-of-Things (IoT)-enabled
autonomous quality monitoring system for gadgets that provide extensive real-time data within healthcare settings.
The suggested construction, comprising an integrity surveillance system and a data analysis module, guarantees
comprehensive insight into medical equipment and enables the prediction of potential problems before their
occurrence.