IoT and Predictive Maintenance in Hospitality Infrastructure

Authors: Mahaboobsubani Shaik

DOI: https://doi.org/10.5281/zenodo.14352270

Short DOI: https://doi.org/g8t7np

Country: India

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Abstract: The integration of IoT technology into predictive maintenance has grown very fast in recent years to become one of the transformative solutions in the optimum performance and maintenance of hospitality infrastructure. The presented work discusses the potentials of IoT predictive maintenance systems in the hospitality business, based on a case study concerning hotel deployment. This article investigates how IoT sensors can help in real-time monitoring for early fault detection, reducing the need for expensive repairs in emergency cases and unplanned downtime. Predictive maintenance systems analyze patterns of energy consumption and equipment performance to provide action-oriented insights on how to anticipate needs for maintenance so that longevity can be improved with operational efficiency. It also compares the benefits of predictive maintenance with traditional methods, showing how this leads to substantial cost reductions, improved resource management, and extended service life of critical infrastructure. Further, the article provides a critical review of the data analysis techniques used in the paper, underlining how machine learning algorithms can be used for enhanced predictive accuracy. The findings also underscore that the integration of IoT significantly improves reliability and prolongs the life of hospitality assets, right from operational savings to better guest experiences.

Keywords: Internet of Things, predictive maintenance, hospitality infrastructure, energy consumption, asset longevity, data analysis, machine learning, cost reduction, operational efficiency, infrastructure optimization, case study, equipment monitoring, resource management, sustainability, downtime reduction, smart hotels, predictive analytics, maintenance strategies

Paper Id: 231794

Published On: 2019-11-07

Published In: Volume 7, Issue 6, November-December 2019

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